Overview
This guide covers essential best practices for getting the most out of Perplexity’s Search API, including query optimization techniques and efficient async usage patterns for high-performance applications.Query Optimization
1
Write specific queries
Use highly specific queries for more targeted results. For example, instead of searching for “AI”, use a detailed query like “artificial intelligence machine learning healthcare applications 2024”.
# Better: Specific query
search = client.search.create(
query="artificial intelligence medical diagnosis accuracy radiology",
max_results=10
)
# Avoid: Vague query
search = client.search.create(
query="AI medical",
max_results=10
)
// Better: Specific query
const search = await client.search.create({
query: "artificial intelligence medical diagnosis accuracy radiology",
max_results: 10
});
// Avoid: Vague query
const search = await client.search.create({
query: "AI medical",
max_results: 10
});
Response — artificial intelligence medical diagnosis accuracy radiology
Response — artificial intelligence medical diagnosis accuracy radiology
{
"id": "f7ff7fde-da09-42fb-8fba-93333178d895",
"results": [
{
"snippet": "",
"title": "The Role of Artificial Intelligence in Diagnostic Radiology - PMC - NIH",
"url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11582495/",
"date": "2024-10-23",
"last_updated": "2026-03-31"
},
{
"snippet": "- **Study shows AI improves performance for some radiologists but worsens it for others.**\n- **Understanding who might benefit from AI and who would not is critical for designing tools that boost human performance.**\n- **The findings underscore the importance of tailored AI-clinician integration over a one-size-fits-all approach.**\nOne of the most touted promises of medical artificial intelligence tools is their ability to augment human clinicians’ performance by helping them interpret images such as X-rays and CT scans with greater precision to make more accurate diagnoses.\nBut the benefits of using AI tools on image interpretation appear to vary from clinician to clinician, according to new research led by investigators at Harvard Medical School, working with colleagues at MIT and Stanford.\n...\nThe study findings suggest that individual clinician differences shape the interaction between human and machine in critical ways that researchers do not yet fully understand.\n...\nIn some instances, the research showed, use of AI can interfere with a radiologist’s performance and interfere with the accuracy of their interpretation.\n“We find that different radiologists, indeed, react differently to AI assistance — some are helped while others are hurt by it,” said co-senior author Pranav Rajpurkar, assistant professor of biomedical informatics in the Blavatnik Institute at HMS.\n“What this means is that we should not look at radiologists as a uniform population and consider just the ‘average’ effect of AI on their performance,” he said.\n...\n“Individual factors and variation would be key in ensuring that AI advances rather than interferes with performance and, ultimately, with diagnosis,” Yu said.\n...\nWhile previous research has shown that AI assistants can, indeed, boost radiologists’ diagnostic performance, these studies have looked at radiologists as a whole without accounting for variability from radiologist to radiologist.\n...\nThe researchers examined how AI tools affected the performance of 140 radiologists on 15 X-ray diagnostic tasks — how reliably the radiologists were able to spot telltale features on an image and make an accurate diagnosis.\n...\nThe effect of AI assistance was inconsistent and varied across radiologists, with the performance of some radiologists improving with AI and worsening in others.\n...\nAI’s effects on human radiologists’ performance varied in often surprising ways.\nFor instance, contrary to what the researchers expected, factors such how many years of experience a radiologist had, whether they specialized in thoracic, or chest, radiology, and whether they’d used AI readers before, did not reliably predict how an AI tool would affect a doctor’s performance.\nAnother finding that challenged the prevailing wisdom: Clinicians who had low performance at baseline did not benefit consistently from AI assistance.\nSome benefited more, some less, and some none at all.\nOverall, however, lower-performing radiologists at baseline had lower performance with or without AI.\nThe same was true among radiologists who performed better at baseline.\nThey performed consistently well, overall, with or without AI.\nThen came a not-so-surprising finding: More accurate AI tools boosted radiologists’ performance, while poorly performing AI tools diminished the diagnostic accuracy of human clinicians.\n...\nThe researchers cautioned that their findings do not provide an explanation for why and how AI tools seem to affect performance across human clinicians differently, but note that understanding why would be critical to ensuring that AI radiology tools augment human performance rather than hurt it.\n...\nApart from improving the accuracy of the AI tools, it’s also important to train radiologists to detect inaccurate AI predictions and to question an AI tool’s diagnostic call, the research team said.\nTo achieve that, AI developers should ensure that they design AI models that can “explain” their decisions.",
"title": "Does AI Help or Hurt Human Radiologists' Performance? It Depends ...",
"url": "https://hms.harvard.edu/news/does-ai-help-or-hurt-human-radiologists-performance-depends-doctor",
"date": "2024-03-19",
"last_updated": "2026-05-16"
},
{
"snippet": "",
"title": "Redefining Radiology: A Review of Artificial Intelligence Integration ...",
"url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC10487271/",
"date": "2023-08-25",
"last_updated": "2026-05-12"
},
{
"snippet": "",
"title": "Diagnostic accuracy of artificial intelligence-assisted radiology ...",
"url": "https://academic.oup.com/bjrai/article/2/1/ubaf016/8322749",
"date": "2025-01-03",
"last_updated": "2026-05-18"
},
{
"snippet": "",
"title": "Revolutionizing Radiology With Artificial Intelligence - PMC - NIH",
"url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11521355/",
"date": "2024-10-29",
"last_updated": "2026-04-05"
},
{
"snippet": "",
"title": "Diagnostic Accuracy and Clinical Value of a Domain-specific Multimodal Generative AI Model for Chest Radiograph Report Generation | Radiology",
"url": "https://pubs.rsna.org/doi/10.1148/radiol.241476",
"date": "2025-03-25",
"last_updated": "2025-03-25"
},
{
"snippet": "",
"title": "Diagnostic accuracy of artificial intelligence-assisted radiology ...",
"url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC13045702/",
"date": "2025-11-13",
"last_updated": "2026-05-02"
},
{
"snippet": "",
"title": "Is Artificial Intelligence the Future of Radiology? Accuracy ...",
"url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11624516/",
"date": "2024-12-06",
"last_updated": "2026-05-05"
},
{
"snippet": "Diagnostic accuracy studies that compared radiologists with and without AI-assistance in cancer diagnostic tasks over all imaging modalities were included.\n...\n**\nResults:\n** Thirty-four studies were included of which 23 were included in meta-analysis.\nEight identified cancers on Chest X-rays, 17 on CT, 9 on MRI.\nPooled sensitivity and specificity were 0.67 (95%CI 0.58-0.74) and 0.82 (95%CI 0.75-0.88), respectively, for clinicians and 0.79 (95%CI 0.71-0.88) and 0.87 (95%CI 0.82-0.91) for AI-assistance.\n17 of 34 studies (50%) had concern of bias with QUADAS-C.\nCLAIM assessment highlighted reporting issues in several domains of methodology in a proportion of studies.\n**\nConclusion:\n** Artificial intelligence assistance tools may benefit clinician diagnostic performance in cancer diagnosis.\nUpdated reporting guidelines may help to overcome potential methodological limitations to clarify AI's value in healthcare.",
"title": "Diagnostic accuracy of artificial intelligence-assisted radiology assessment of cancer: a systematic review - PubMed",
"url": "https://pubmed.ncbi.nlm.nih.gov/42064000/",
"date": "2025-11-13",
"last_updated": "2026-05-02"
},
{
"snippet": "",
"title": "Artificial Intelligence-Empowered Radiology—Current Status and ...",
"url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC11816879/",
"date": "2025-01-24",
"last_updated": "2026-04-07"
}
],
"server_time": null
}
Response — AI medical
Response — AI medical
{
"id": "0b4ab51d-19e1-40f1-91b4-a5604cd5f33d",
"results": [
{
"snippet": "The FDA encourages the development of innovative, safe, and effective medical devices, including devices that incorporate artificial intelligence (AI).\nThe AI-Enabled Medical Device List is a resource intended to identify AI-enabled medical devices that are authorized for marketing in the United States.\nDigital health innovators can refer to this list to gain insights into the current device landscape and regulatory expectations, which can help foster innovation and ensure public safety.\nThis list can also provide transparency for healthcare providers and patients to clearly identify when medical devices use AI technologies.\n...\n- To support transparency in the use of modern AI technologies, the FDA will explore methods to identify and tag medical devices that incorporate foundation models encompassing a wide range of AI systems, from large language models (LLMs) to multimodal architectures.\nThis identification will help innovators, healthcare providers, and patients recognize when LLM-based functionality is present in a medical device.\nTo facilitate the FDA’s development of methods to identify AI-enabled medical devices more easily, including identifying those devices incorporating LLM-based functionality in a future update of this list, sponsors are encouraged to include appropriate information in their public summaries.",
"title": "Artificial Intelligence-Enabled Medical Devices - FDA",
"url": "https://www.fda.gov/medical-devices/software-medical-device-samd/artificial-intelligence-enabled-medical-devices",
"date": "2026-04-03",
"last_updated": "2026-05-21"
},
{
"snippet": "AI has the potential to help save lives by transforming healthcare and medicine through the creation of more personalized, accessible and effective solutions.\nIn collaboration with healthcare providers, researchers and industry partners, we’ve published research, created open-source tools, and built AI systems that have the potential to positively impact health outcomes for people globally.\n...\nBuilding clinical-grade AI presents a significant challenge: organizations with specialized health data often lack the resources to create foundational models from scratch, while general-purpose models may not meet the standards for safe and reliable use in patient care.\nTo accelerate innovation and foster novel solutions, we’ve released powerful, open-weight models to help developers build AI models for healthcare.\nThis includes MedGemma, our most capable open model for multimodal medical text and image comprehension, and TxGemma, a collection of open models to accelerate the development of therapeutics.\n...\nWe’re imagining ways that AI systems could be useful conversational partners for clinicians and patients.\nArticulate Medical Intelligence Explorer (AMIE) is an experimental system which aims to combine clinical reasoning with conversational empathy.\nAMIE is an exploration into the art of the possible, where our research is testing novel ways for AI to safely support clinicians and patients.\n...\nOur research extends to the future of personalized wellness, exploring how AI can help interpret complex data from wearables and devices.\nThis includes our work on our foundational Large Sensor Model (LSM), trained on the largest dataset of its kind to decode signals like heart rate and activity levels with remarkable accuracy.\nAdditionally, our Personal Health LLM (PH-LLM), a version of the Gemini model fine-tuned for health, aims to empower people to understand their health and wellness data, set goals, and make informed decisions.\n...\nWe’ve developed an AI system to interpret chest X-ray scans for early signs of TB.\nWe’ve been working with Apollo Radiology International over the past few years to validate our AI systems in regions where they can have the most impact.\nOver the next 10 years, Apollo Radiology International will use these models to provide 3 million free AI-powered screenings for TB, lung cancer, and breast cancer — helping hundreds of thousands more people across India access timely care.\n...\nOpen Health Stack is a suite of open-source building blocks built on an interoperable data standard.\nThis suite of components makes it easier for developers to quickly build apps allowing healthcare workers to access the information and insights they need to make informed decisions.",
"title": "Health AI - Google AI",
"url": "https://ai.google/health/",
"date": null,
"last_updated": "2026-05-02"
},
{
"snippet": "AI Health Main content start Back to Top",
"title": "AI Health",
"url": "https://aihealth.stanford.edu",
"date": null,
"last_updated": "2026-03-05"
},
{
"snippet": "",
"title": "The benefits of AI in health care and how doctors are using AI",
"url": "https://www.youtube.com/watch?v=rfMr35T4vhw",
"date": "2025-05-12",
"last_updated": "2026-03-04"
},
{
"snippet": "Redefine GI Cancer Diagnostics with AI\nJapanese Innovation Revolutionizing Medicine World-wide\n...\nAI Medical Service was founded to take on these issues with AI (artificial intelligence), contributing to advances in endoscopic medicine and the elimination of GI cancers through early-stage detection.\n...\nThe journey we are embarked on is to provide the world with unprecedented Japanese AI medical software.\n...\ngastroAI media is a specialized media site for endoscopists.\nUtilizing our connections with endoscopists working on the front lines, we provide information on diagnosis, insertion, and observation know-how for early stage cancer, as well as research information on endoscopic AI.",
"title": "AI Medical Service Corporation｜Saving Patients Around the World",
"url": "https://en.ai-ms.com",
"date": "2026-05-15",
"last_updated": "2026-05-16"
},
{
"snippet": "",
"title": "Artificial intelligence in healthcare: transforming the practice of ...",
"url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8285156/",
"date": null,
"last_updated": "2026-05-15"
},
{
"snippet": "",
"title": "How AI is transforming medicine - Harvard Gazette",
"url": "https://news.harvard.edu/gazette/story/2025/03/how-ai-is-transforming-medicine-healthcare/",
"date": "2025-03-20",
"last_updated": "2026-05-21"
}
],
"server_time": null
}
Specific queries with context, time frames, and precise terminology yield more relevant and actionable results.
2
Use multi-query for comprehensive research
Break your main topic into related sub-queries to cover all aspects of your research. Use the multi-query search feature to run multiple related queries in a single request for more comprehensive and relevant information.
from perplexity import Perplexity
client = Perplexity()
# Comprehensive research with related queries
search = client.search.create(
query=[
"artificial intelligence medical diagnosis accuracy 2024",
"machine learning healthcare applications FDA approval",
"AI medical imaging radiology deployment hospitals"
],
max_results=5
)
# Access results
for result in search.results:
print(f" {result.title}: {result.url}")
import Perplexity from '@perplexity-ai/perplexity_ai';
const client = new Perplexity();
// Comprehensive research with related queries
const search = await client.search.create({
query: [
"artificial intelligence medical diagnosis accuracy 2024",
"machine learning healthcare applications FDA approval",
"AI medical imaging radiology deployment hospitals"
],
max_results: 5
});
// Access results
search.results.forEach(result => {
console.log(` ${result.title}: ${result.url}`);
});
You can include up to 5 queries in a single multi-query request for efficient batch processing.
3
Handle rate limits efficiently
Implement exponential backoff for rate limit errors and use appropriate batching strategies.
import time
import random
from perplexity import Perplexity, RateLimitError
def search_with_retry(client, query, max_retries=3):
for attempt in range(max_retries):
try:
return client.search.create(query=query)
except RateLimitError:
if attempt < max_retries - 1:
# Exponential backoff with jitter
delay = (2 ** attempt) + random.uniform(0, 1)
time.sleep(delay)
else:
raise
client = Perplexity()
# Usage
try:
search = search_with_retry(client, "AI developments")
for result in search.results:
print(f"{result.title}: {result.url}")
except RateLimitError:
print("Maximum retries exceeded for search")
import Perplexity from '@perplexity-ai/perplexity_ai';
async function searchWithRetry(
client: Perplexity,
query: string,
maxRetries: number = 3
) {
for (let attempt = 0; attempt < maxRetries; attempt++) {
try {
return await client.search.create({ query });
} catch (error) {
if (error instanceof Perplexity.RateLimitError && attempt < maxRetries - 1) {
// Exponential backoff with jitter
const delay = (2 ** attempt) + Math.random();
await new Promise(resolve => setTimeout(resolve, delay * 1000));
} else {
throw error;
}
}
}
throw new Error("Max retries exceeded");
}
const client = new Perplexity();
// Usage
try {
const search = await searchWithRetry(client, "AI developments");
search.results.forEach(result => {
console.log(`${result.title}: ${result.url}`);
});
} catch (error) {
console.log("Maximum retries exceeded for search");
}
4
Process concurrent searches efficiently
Use async for concurrent requests while respecting rate limits.
import asyncio
from perplexity import AsyncPerplexity
async def batch_search(queries, batch_size=3, delay_ms=1000):
async with AsyncPerplexity() as client:
results = []
for i in range(0, len(queries), batch_size):
batch = queries[i:i + batch_size]
batch_tasks = [
client.search.create(query=query, max_results=5)
for query in batch
]
batch_results = await asyncio.gather(*batch_tasks)
results.extend(batch_results)
# Add delay between batches
if i + batch_size < len(queries):
await asyncio.sleep(delay_ms / 1000)
return results
# Usage
queries = ["AI developments", "climate change", "space exploration"]
results = asyncio.run(batch_search(queries))
print(f"Processed {len(results)} searches")
import Perplexity from '@perplexity-ai/perplexity_ai';
async function batchSearch(
queries: string[],
batchSize: number = 3,
delayMs: number = 1000
) {
const client = new Perplexity();
const results = [];
for (let i = 0; i < queries.length; i += batchSize) {
const batch = queries.slice(i, i + batchSize);
const batchPromises = batch.map(query =>
client.search.create({
query,
max_results: 5
})
);
const batchResults = await Promise.all(batchPromises);
results.push(...batchResults);
// Add delay between batches
if (i + batchSize < queries.length) {
await new Promise(resolve => setTimeout(resolve, delayMs));
}
}
return results;
}
// Usage
const queries = ["AI developments", "climate change", "space exploration"];
const results = await batchSearch(queries);
console.log(`Processed ${results.length} searches`);
Async Usage
For high-performance applications requiring concurrent requests, use the async client:import asyncio
from perplexity import AsyncPerplexity
async def main():
async with AsyncPerplexity() as client:
# Concurrent searches for better performance
tasks = [
client.search.create(
query="artificial intelligence industry adoption trends across sectors",
max_results=5
),
client.search.create(
query="machine learning research breakthroughs in deep learning",
max_results=5
),
client.search.create(
query="deep learning applications across industries",
max_results=5
)
]
results = await asyncio.gather(*tasks)
for i, search in enumerate(results):
print(f"Query {i+1} results:")
for result in search.results:
print(f" {result.title}: {result.url}")
print("---")
asyncio.run(main())
import Perplexity from '@perplexity-ai/perplexity_ai';
const client = new Perplexity();
async function main() {
// Concurrent searches for better performance
const tasks = [
client.search.create({
query: "artificial intelligence industry adoption trends across sectors",
max_results: 5
}),
client.search.create({
query: "machine learning research breakthroughs in deep learning",
max_results: 5
}),
client.search.create({
query: "deep learning applications across industries",
max_results: 5
})
];
const results = await Promise.all(tasks);
results.forEach((search, i) => {
console.log(`Query ${i+1} results:`);
search.results.forEach(result => {
console.log(` ${result.title}: ${result.url}`);
});
console.log("---");
});
}
main();
const Perplexity = require('@perplexity-ai/perplexity_ai');
const client = new Perplexity();
async function main() {
// Concurrent searches for better performance
const tasks = [
client.search.create({
query: "artificial intelligence industry adoption trends across sectors",
max_results: 5
}),
client.search.create({
query: "machine learning research breakthroughs in deep learning",
max_results: 5
}),
client.search.create({
query: "deep learning applications across industries",
max_results: 5
})
];
const results = await Promise.all(tasks);
results.forEach((search, i) => {
console.log(`Query ${i+1} results:`);
search.results.forEach(result => {
console.log(` ${result.title}: ${result.url}`);
});
console.log("---");
});
}
main();
Response — artificial intelligence industry adoption trends across sectors
Response — artificial intelligence industry adoption trends across sectors
{
"id": "9bf13556-1586-4d9e-bfc2-29595af3eac8",
"results": [
{
"snippet": "",
"title": "AI Adoption Across Industries: Trends You Don't Want to Miss in 2025",
"url": "https://www.coherentsolutions.com/insights/ai-adoption-trends-you-should-not-miss-2025",
"date": "2026-04-28",
"last_updated": "2026-05-21"
},
{
"snippet": "",
"title": "The State of AI: Global Survey 2025 - McKinsey",
"url": "https://www.mckinsey.com/capabilities/quantumblack/our-insights/the-state-of-ai",
"date": "2025-11-05",
"last_updated": "2026-05-21"
},
{
"snippet": "Adoption of AI in the U.S. overall is relatively low with only 5% of businesses utilizing the technology currently.\nHowever, the adoption rate varies by industry (Figure 1).\nInformation and Professional, Scientific, and Technical Service sectors lead the way with 18% and 12% of businesses, respectively, reporting current use of AI, whereas Agriculture and Construction are sectors with low AI adoption at 1% each.\nBusinesses report that future use of AI within the next six months is projected to increase across all sectors, though the percentages again vary widely.\nInterestingly, AI adoption patterns across sectors correspond with the occupational impact prediction that white-collar, office jobs will be more impacted by AI than physical, outdoor jobs.\n...\nThe top two applications for future AI utilization in the U.S are marketing automation and data analytics.\nThese trends generally align with the North Carolina data, where 41% of NC businesses planning to use AI reported using it for marketing automation and 28% for data analytics.\nAn examination of U.S. sector-specific data (Figure 2) reveals that numerous industries anticipate using these AI applications.\nFor instance, AI-driven marketing automation is projected not only in sectors such as Real Estate, Retail, and Accommodation and Food Services but also in less obvious sectors like Construction, Education, and Agriculture.\nSimilarly for data analytics, industries such as Utilities, Management, and Transportation and Warehousing are leading in projected AI use.\nHowever, even in sectors like Arts, Entertainment, and Recreation about one in five businesses that report future AI usage, expect to use AI for data analytics by the end of summer.\n...\nAmong U.S. businesses expecting to incorporate AI in the next six months, 88% reported that their total employment levels will remain unchanged.\nFigure 3 shows the industry breakdown of the small percentage of businesses reporting an increase or decrease in total employment.\nThe Information sector, the leading adopter of AI, also tops the list for projected employment increases, with 10% of businesses expecting growth, while only 4% anticipate a decline, placing it among the lowest for employment decreases.\nA similar trend is evident in the Professional, Scientific, and Technical Services sector.\n...\nWhile overall AI adoption remains low, certain sectors like Information and Professional, Scientific, and Technical Services are leading the way.\nThe primary AI applications, marketing automation and data analytics, show broad applicability across various sectors, including some unexpected ones like Construction and Arts, Entertainment, and Recreation.\nAnd finally, while most businesses do not anticipate changes in employment levels, sectors with higher AI adoption rates, such as Information and Professional, Scientific, and Technical Services, may be showing signs of optimism for potential employment growth.",
"title": "What Industries Are Using AI? Current Use and Future Expectations",
"url": "https://www.commerce.nc.gov/news/the-lead-feed/what-industries-are-using-ai",
"date": "2024-06-04",
"last_updated": "2026-05-03"
},
{
"snippet": "The research shows variation in AI adoption, according to Kristina McElheran, a visiting scholar with the MIT Initiative on the Digital Economy and the paper’s lead author.\nJust 6% of U.S. companies used AI in 2017, the researchers found, and AI use was concentrated in larger companies and in industries such as manufacturing and information technology.\nAdoption was also clustered in some “superstar” cities, such as San Francisco, San Antonio, and Nashville.\n...\nThe finding that just 6% of companies reported using AI in 2017 is still relevant today, McElheran said, pointing to a November 2023 Census Bureau survey that showed that fewer than 4% of companies use AI to produce goods and services.\nThe initial, in-depth survey showed other early trends:\n- AI use was highest among large companies.\nMore than 50% of companies with more than 5,000 employees were using AI, as were more than 60% of companies with more than 10,000 employees.\n- Use varied among sectors.\nAbout 12% of firms in manufacturing, information services, and health care were using AI, compared with 4% in construction and retail.\n- AI adoption is happening in some superstar cities, but it has also clustered in some unlikely places.\nThese include manufacturing hubs in the Midwest as well as Southern cities with fewer companies overall than tech hubs in Silicon Valley, the Boston area, or New York City.\n“Use of AI in production is happening in different places than just the areas that are inventing and commercializing AI-based technologies,” McElheran said.\n...\nThe researchers found that startups using AI were more likely to have younger, more highly educated, and more highly experienced leaders than startups that were not using AI.\nVenture capital backing and a focus on process innovation were also associated with AI adoption.\n...\nSome of those AI users are in sectors not typically associated with cutting-edge technology, such as manufacturing and health care.\nThe former is closely linked to manufacturing’s use of robotics.",
"title": "The who, what, and where of AI adoption in America | MIT Sloan",
"url": "https://mitsloan.mit.edu/ideas-made-to-matter/who-what-and-where-ai-adoption-america",
"date": "2024-02-07",
"last_updated": "2026-05-18"
},
{
"snippet": "",
"title": "The Adoption of Artificial Intelligence in Firms - OECD",
"url": "https://www.oecd.org/en/publications/the-adoption-of-artificial-intelligence-in-firms_f9ef33c3-en.html",
"date": "2025-05-02",
"last_updated": "2025-11-18"
},
{
"snippet": "",
"title": "AI Adoption by Industry: How Different Sectors Are Using AI at Scale in 2026",
"url": "https://codewave.com/insights/ai-adoption-industry-trends-insights/",
"date": "2026-02-26",
"last_updated": "2026-05-21"
},
{
"snippet": "",
"title": "The Fed - Monitoring AI Adoption in the US Economy",
"url": "https://www.federalreserve.gov/econres/notes/feds-notes/monitoring-ai-adoption-in-the-u-s-economy-20260403.html",
"date": "2026-03-04",
"last_updated": "2026-05-21"
},
{
"snippet": "",
"title": "AI Adoption Statistics Q1 2026: All Figures - Vention",
"url": "https://ventionteams.com/solutions/ai/adoption-statistics",
"date": "2024-06-26",
"last_updated": "2026-05-03"
},
{
"snippet": "",
"title": "AI Adoption by Industry",
"url": "https://www.netguru.com/blog/ai-adoption-statistics",
"date": "2025-12-15",
"last_updated": "2026-05-21"
}
],
"server_time": null
}
Response — machine learning research breakthroughs in deep learning
Response — machine learning research breakthroughs in deep learning
{
"id": "f22580ab-72a2-4038-bc35-8151455664a7",
"results": [
{
"snippet": "",
"title": "Timeline of machine learning - Wikipedia",
"url": "https://en.wikipedia.org/wiki/Timeline_of_machine_learning",
"date": "2016-06-16",
"last_updated": "2026-03-09"
},
{
"snippet": "*Twenty years ago, Google started using machine learning, and 10 years ago, it helped spur rapid progress in AI using deep learning.\nJeff Dean and Marian Croak of Google Research take a look at how we’ve innovated on these techniques and applied them in helpful ways, and look ahead to a responsible and inclusive path forward.*\n...\nBut it would be another decade before we had enough computing power to revive a more computationally-intensive machine learning approach called deep learning.\nDeep learning uses neural networks with multiple layers (thus the “deep”), so it can learn not just simple statistical patterns, but can learn subtler patterns of patterns — such as what’s in an image or what word was spoken in some audio.\nOne of our first publications in 2012 was on a system that could find patterns among millions of frames from YouTube videos.\nThat meant, of course, that it learned to recognize cats.\nTo get to the helpful features you use every day — searchable photo albums, suggestions on email replies, language translation, flood alerts, and so on — we needed to make years of breakthroughs on top of breakthroughs, tapping into the best of Google Research in collaboration with the broader research community.\nLet me give you just a couple examples of how we’ve done this.\n**A big moment for image recognition**\nIn 2012, a paper wowed the research world for making a huge jump in accuracy on image recognition using deep neural networks, leading to a series of rapid advances by researchers outside and within Google.\nFurther advances led to applications like Google Photos in 2015, letting you search photos by what’s in them.\nWe then developed other deep learning models to help you find addresses in Google Maps, make sense of videos on YouTube, and explore the world around you using Google Lens.\nBeyond our products, we applied these approaches to health-related problems, such as detecting diabetic retinopathy in 2016, and then cancerous cells in 2017, and breast cancer in 2020.\nBetter understanding of aerial imagery through deep learning let us launch flood forecasting in 2018, now expanded to cover more than 360 million people in 2021.\nIt’s been encouraging to see how helpful these advances in image recognition have been.\nSimilarly, we’ve used deep learning to accelerate language understanding.\nWith sequence-to-sequence learning in 2014, we began looking at how to understand strings of text using deep learning.\nThis led to neural machine translation in Google Translate in 2016, which was a massive leap in quality, particularly for less prevalent languages.\nWe developed neural language models further for Smart Reply in Gmail in 2017, which made it easier and faster for you to knock through your email, especially on mobile.\nThat same year, Google invented Transformers, leading to BERT in 2018, then T5, and in 2021 MUM, which lets you ask Google much more nuanced questions.\nAnd with “sparse” models like GShard, we can dramatically improve on tasks like translation while using less energy.\nWe’ve driven a similar arc in understanding speech.\nIn 2012, Google used deep neural networks to make major improvements to speech recognition on Android.\nWe kept advancing the state of the art with higher-quality, faster, more efficient speech recognition systems.\nBy 2019, we were able to put the entire neural network on-device so you could get accurate speech recognition even without a connection.\nAnd in 2021, we launched Live Translate on the Pixel 6 phone, letting you speak and be translated in 48 languages -- all on-device, while you’re traveling with no Internet.\nProject Relate: A communication tool for people with speech impairments.\nML-based flood forecasting helps equip those in harm’s way with accurate and detailed alerts.\nGoogle Health's AI system helps radiologists identify cancer in mammograms with greater accuracy.\n...\nAs our research goes forward, we’re balancing more immediately applied research with more exploratory fundamental research.\nSo we’re looking at how, for example, AI can aid scientific discovery, with a project like mapping the brain of a fly, which could one day help better understand and treat mental illness in people.\nWe’re also pursuing quantum computing, which will likely take a decade or longer to reach wide-scale applications.\nThis is why we publish nearly 1000 papers a year, including around 200 related to responsible AI, and we’ve given over 6500 grants to external researchers over the past decade and a half.\n...\nOne example is Project Relate, which uses machine learning to help people with speech impairments communicate and use technology more easily.",
"title": "A decade in deep learning, and what's next - Google Blog",
"url": "https://blog.google/innovation-and-ai/products/decade-deep-learning-and-whats-next/",
"date": "2021-11-18",
"last_updated": "2026-05-02"
},
{
"snippet": "",
"title": "Deep Learning: A Comprehensive Overview on Techniques ... - PMC",
"url": "https://pmc.ncbi.nlm.nih.gov/articles/PMC8372231/",
"date": "2021-08-18",
"last_updated": "2026-05-23"
},
{
"snippet": "At UC San Diego, AI is empowering bold advances in science, education, public safety, health and the arts.\n...\nFrom uncovering hidden disease mechanisms to guiding new therapies and medical technologies, UC San Diego researchers are harnessing AI to propel humanity toward a future in which diseases are mitigated or eradicated.\nWhat was once science fiction is now possible.\nAs a tool, AI speeds diagnoses, offers a better understanding of the human body and enables more innovative treatments.\n...\nA gene once thought to be just a biomarker for Alzheimer’s disease is actually one of its causes, according to the work of UC San Diego bioengineers.\nUsing AI to model the 3D structures of proteins, the team discovered that the gene PHGDH has a hidden “moonlighting” role: It disrupts how brain cells switch genes on and off, a disturbance that can fuel the disease.\n...\nJoe Pogliano, professor, and Kit Pogliano, dean and distinguished professor, both in the School of Biological Sciences, worked with Linnaeus Bioscience and Seattle Children’s Research Institute to develop MycoBCP, a new AI-powered tool that could accelerate the search for TB treatments.\nThe technology builds on the Poglianos’ earlier bacterial cytological profiling method, which quickly shows how antibiotics work.\nBy pairing that method with deep learning, researchers could detect subtle changes in TB cells that would otherwise escape the human eye — revealing how potential drugs act on the pathogen.\n“This is the first time machine learning has been applied this way to bacteria,” says Joe Pogliano.\nThe advance, funded in part by the Gates Foundation, could fast-track the development of urgently needed TB therapies.\n...\nBy recording signals from outside of heart muscle cells and using AI to reconstruct what’s happening inside these cells, engineers were able to monitor heart activity with remarkable accuracy — without invasive methods or physically penetrating the cells.\nThe breakthrough offers safer, faster insights into how heart cells function, communicate and respond to new drugs.\n...\nResearchers have developed advanced deep-learning techniques that could revolutionize treatment planning for breast cancer radiotherapy — making it faster and improving its quality.\nThe approach reduced errors in radiation doses to critical organs, such as the heart and lungs.\n...\nUC San Diego engineers have created a flexible patch — a wireless, skin-mounted ultrasound device — that monitors muscle activity in real time to control a robotic arm, among other uses.\nTo extract additional insights from these muscle signals, the researchers developed an AI algorithm that maps the signals to their corresponding muscle distributions.\nCompact, battery-powered and designed for long-term wear, the technology could open new possibilities in health care monitoring and human-machine interaction.\n...\nIn a unique and long-standing collaboration, UC San Diego electrical engineering graduate students have been embedded with the Jacobs Retina Center at Shiley Eye Institute to develop better computer vision, AI and image-processing tools.\nThese will help physicians diagnose patients with greater speed and accuracy, predict the most effective treatments, and aid in the development of new treatments.\n...\nUC San Diego researchers are using AI to better understand Earth’s changing environment.\nThese innovations provide faster forecasts, actionable insights and tools that help protect communities and the planet.\n...\nRunning global climate simulations can take weeks on supercomputers, limiting the number of scenarios scientists can explore.\nResearchers at UC San Diego and the Allen Institute for AI have developed a new model, Spherical DYffusion, that projects 100 years of climate patterns in just 25 hours.\nBy combining generative AI techniques with physics-based data, the model delivers results 25 times faster than current methods without the need for massive supercomputers.\nThe breakthrough could provide scientists and policymakers with faster and more flexible tools for anticipating the long-term effects of climate change.\n...\nWith a growing network of more than 1,200 natural-hazard monitoring cameras spanning remote mountaintops to wildland-urban interfaces, ALERTCalifornia collects real-time data and utilizes AI that helps emergency managers spot smoke, monitor fires and plan evacuations.\nUC San Diego’s WIFIRE team at the San Diego Supercomputer Center utilizes those camera and sensor feeds to pinpoint wildfire ignition locations and create predictive models that map how a fire will spread.\n...\nThese models are further refined by AI-powered aerial sensing — including infrared imaging — to track fire perimeters through thick smoke and give responders a strategic advantage as wildfires progress.",
"title": "Nine Breakthroughs Made Possible by AI - UC San Diego Today",
"url": "https://today.ucsd.edu/story/nine-breakthroughs-made-possible-by-ai",
"date": "2025-11-21",
"last_updated": "2026-05-26"
},
{
"snippet": "",
"title": "Bolder breakthroughs, bigger impact - Google Research 2025",
"url": "https://research.google/blog/google-research-2025-bolder-breakthroughs-bigger-impact/",
"date": null,
"last_updated": "2026-05-25"
},
{
"snippet": "",
"title": "Review Paper on Machine Learning Breakthroughs",
"url": "https://www.ijfmr.com/papers/2024/3/22369.pdf",
"date": null,
"last_updated": "2025-09-23"
},
{
"snippet": "",
"title": "A Brief Overview of Deep Learning — Making Things Think - Holloway",
"url": "https://www.holloway.com/g/making-things-think/sections/a-brief-overview-of-deep-learning",
"date": "2022-11-02",
"last_updated": "2026-04-12"
}
],
"server_time": null
}
Response — deep learning applications across industries
Response — deep learning applications across industries
{
"id": "6c99d1de-cd0c-4795-970a-5b14387c296c",
"results": [
{
"snippet": "Deep learning applications have a broad reach, spanning various fields and industries, and are an important part of artificial intelligence.\nDeep learning even plays a role in some of the products and technologies you likely use daily.\nWith a design inspired by the human brain, deep learning networks can process significant amounts of information, helping to make use of all the data that’s accessible in today's world so companies can improve their product offerings, learn more about consumers, and solve problems.\n...\n## 10 deep learning applications\nDeep learning applications are making an impact across many different industries.\nYou might even already use some of these applications in your everyday life.\nLet’s examine ten examples highlighting deep learning’s broad use to understand it better.\n### 1.\nFraud detection\nDeep learning algorithms can identify security issues to help protect against fraud.\nFor example, deep learning algorithms can detect suspicious attempts to log into your accounts and notify you, as well as inform you if your chosen password isn’t strong enough.\n### 2.\nCustomer service\nYou may have seen or used customer service help online and interacted with a chatbot to help answer your questions or utilized a virtual assistant on your smartphone.\nDeep learning allows these systems to learn over time to respond.\n### 3.\nFinancial services\nSeveral financial services can rely on assistance from deep learning.\nPredictive analytics helps support investment portfolios and trading assets in the stock market, as well as allowing banks to mitigate risk relating to loan approvals.\n### 4.\nNatural language processing\nNatural language processing is an important part of deep learning applications that rely on interpreting text and speech.\nCustomer service chatbots, language translators, and sentiment analysis are all examples of applications benefitting from natural language processing.\n...\n### 5.\nFacial recognition\nAn area of deep learning known as computer vision allows deep learning algorithms to recognize specific features in pictures and videos.\nWith this technique, you can use deep learning for facial recognition, identifying you by your own unique features.\n...\n### 6.\nSelf-driving vehicles\nAutonomous vehicles use deep learning to learn how to operate and handle different situations while driving, and it allows vehicles to detect traffic lights, recognize signs, and avoid pedestrians.\n### 7.\nPredictive analytics\nDeep learning models can analyze large amounts of historical information to make accurate predictions about the future.\nPredictive analytics helps businesses in several aspects, including forecasting revenue, product development, decision-making, and manufacturing.\n### 8.\nRecommender systems\nOnline services often use recommender systems with enhanced capabilities provided by deep learning models.\nWith enough data, these deep learning models can predict the probabilities of certain interactions based on the history of previous interactions.\nIndustries such as streaming services, e-commerce, and social media implement recommender systems.\n### 9.\nHealth care\nDeep learning applications in the health care industry serve multiple purposes.\nNot only can they assist in developing treatment solutions, but deep learning algorithms are also capable of understanding medical images and helping doctors diagnose patients by detecting cancer cells.\n...\n### 10.\nIndustrial\nDeep learning applications in industrial automation help keep workers safe in factories by enabling machines to detect dangerous situations, such as when objects or people are too close to the machines.\n###",
"title": "10 Examples of Deep Learning Applications | Coursera",
"url": "https://www.coursera.org/articles/deep-learning-applications",
"date": "2024-03-22",
"last_updated": "2026-05-18"
},
{
"snippet": "",
"title": "Top 25 Deep Learning Applications Used Across Industries",
"url": "https://www.simplilearn.com/tutorials/deep-learning-tutorial/deep-learning-applications",
"date": "2020-11-13",
"last_updated": "2026-03-28"
},
{
"snippet": "",
"title": "Deep Learning Applications in...",
"url": "https://www.neuralconcept.com/post/digital-thread-deep-learning-applications-in-engineering",
"date": "2026-05-21",
"last_updated": "2026-05-25"
},
{
"snippet": "",
"title": "Common Deep Learning...",
"url": "https://builtin.com/artificial-intelligence/deep-learning-applications",
"date": "2023-05-24",
"last_updated": "2025-10-29"
},
{
"snippet": "The widespread applications of deep learning span diverse industries, including healthcare, finance, agriculture, and natural language processing, signifying its transformative impact.\n...\nThis article delves into six prevalent applications of deep learning, namely computer vision, natural language processing, healthcare, finance, agriculture, and cybersecurity.\nWe will discuss how deep learning is being used in these fields and the benefits it provides.\n...\nA prominent application of deep learning in NLP is machine translation, where models are trained on extensive datasets of human language to accurately translate text from one language to another.\n...\nDeep learning is a game-changer in healthcare, particularly in enhancing medical imaging such as CT and MRI scans, enabling specialists to formulate more precise and personalized treatment plans.\n...\nDeep learning stands as a formidable force in revolutionizing the financial sector, offering a diverse array of applications that enhance decision-making, risk management, fraud detection, and customer experience.\n...\nA pivotal application lies in crop monitoring, where deep learning models are trained to scrutinize satellite, drone, and other remote sensing data to detect changes in crop health and predict yields.\n...\nDeep learning is a valuable asset in bolstering threat detection and response within cybersecurity.",
"title": "Top 6 Transformative Applications of Deep Learning Across Industries",
"url": "https://vngcloud.vn/blog/top-6-transformative-applications-of-deep-learning-across-industries",
"date": "2024-01-24",
"last_updated": "2026-04-09"
},
{
"snippet": "Deep learning and neural networks have revolutionized various industries, offering unprecedented data analysis, pattern recognition, and automation capabilities.\nThis blog explores how these technologies are transforming different sectors and what the future might hold.\n### Deep Learning Applications in the Healthcare Industry\n- **Medical Imaging and Diagnostics**\nDeep learning algorithms are increasingly used in medical imaging to improve and enhance the accuracy and speed of diagnostics.\nThese algorithms can interpret medical images such as MRIs, CT scans, and X-rays to detect tumors, abnormalities, and other conditions.\nFor example, convolutional neural networks (CNNs) have been trained to identify early signs of breast cancer in mammograms, sometimes outperforming human radiologists.\nAdditionally, deep learning models assist in segmenting and classifying tissues, making the diagnostic process more efficient.\n- **Drug Discovery**\nIn the pharmaceutical industry, deep learning accelerates drug discovery by analyzing vast datasets of chemical compounds and biological interactions.\nRecurrent neural networks (RNNs) and generative adversarial networks (GANs) can predict the efficacy and potential side effects of new drugs, significantly shortening the research timeline.\nThese models simulate how different compounds interact with targets in the body, aiding in the identification of promising drug candidates.\n- **Personalized Medicine**\nPersonalized medicine leverages deep learning to tailor treatments to the specific needs of patients based on their genetic data.\nBy analyzing individual genetic makeup, neural networks can predict how patients will respond to different treatments, improving outcomes.\nThis approach is particularly valuable in oncology, where personalized treatment plans can be developed based on the genetic profile of a patient’s tumor.\n- **Clinical Documentation**\nNLP models help in transcribing and analyzing clinical notes, making it easier to extract valuable insights and improve patient care.\n- **Virtual Assistants**\nAI-powered virtual assistants can help in patient management by answering queries, scheduling appointments, and providing medical advice based on patient data.\n### Deep Learning Applications in the Finance Industry\n- **Fraud Detection**\nFinancial institutions use deep learning models to detect fraudulent transactions by analyzing patterns in transaction data.\nThese models, often based on deep belief networks (DBNs) and autoencoders, can identify anomalies that may indicate fraud.\nBy continuously learning from new data, these neural networks improve their detection capabilities over time, reducing false negatives and seizing increasingly higher numbers of fraudulent activities.\n- **Algorithmic Trading**\nHigh-frequency and high-volume trading systems are nowadays backed by deep learning algorithms that pore over market data, analyzing them in real-time, identifying patterns, trends, and indicators to execute trades at optimal moments to obtain the highest returns.\nLong short-term memory (LSTM) networks and other RNNs can process vast amounts of financial data, identifying market trends and patterns that human traders might miss.\nThese algorithms are highly reactive and can respond to market changes within milliseconds, capitalizing on opportunities that can be missed within the blink of an eye.\n- **Risk Management**\nNeural networks assess the risk of investments by analyzing historical data and market conditions.\nThis enables financial institutions to make informed decisions and reduce potential losses.\nFor example, deep learning models can predict credit default risks by evaluating a borrower’s financial history and current market trends, leading to more accurate credit scoring and loan approvals.\n### Deep Learning Applications in the Retail Industry\n- **Customer Insights and Personalization**\nRetailers use deep learning to analyze customer behavior and preferences, allowing for highly personalized marketing.\nBy examining purchase history, browsing patterns, and social media activity, neural networks can predict what products customers are likely to buy and tailor recommendations accordingly which has been observed to have a hugely positive impact on customer satisfaction and sales.\n- **Inventory Management**\nDeep learning algorithms help retailers optimize inventory levels by predicting demand for various products.\nThese models, often based on LSTM networks, consider factors like seasonal trends, market conditions, and historical sales data.\nThis ensures that popular items are always in stock while minimizing overstock and reducing storage costs, improving overall efficiency.\n- **Visual Search**\nE-commerce platforms utilize deep learning for visual search capabilities, enabling customers to search for products using images.\nConvolutional neural networks analyze visual features to identify and recommend similar products.\nThis technology enhances the shopping experience by making it easier for customers to find products they are interested in, even if they do not know the exact name.\n### Deep Learning Applications in the Manufacturing Industry\n- **Predictive Maintenance**\nIn manufacturing, deep learning is used for predictive maintenance, which involves predicting when equipment is likely to fail and proactively performing maintenance.\nBy analyzing sensor data from machinery, neural networks can identify patterns that precede breakdowns.\nThis has been observed to reduce downtime, increase equipment lifespan, and lower the cost of maintenance.\n- **Quality Control**\nDeep learning algorithms improve quality control by analyzing images of products to detect defects.\nConvolutional neural networks can identify flaws that are difficult for human inspectors to see, ensuring that only high-quality products reach the market.\nThis not only enhances product reliability but also reduces waste and rework costs.\n- **Supply Chain Optimization**\nDeep learning helps optimize supply chains by analyzing data from various sources, including weather reports, transportation schedules, and market demand.\nThese models, often based on reinforcement learning, enable manufacturers to make informed decisions about production and distribution, improving efficiency and reducing costs.\nFor example, neural networks can predict the optimal time to reorder raw materials based on historical usage patterns and current market conditions.\n### Deep Learning Applications in the Autonomous Driving Industry\n- **Object Detection and Recognition**\nDeep learning is the most crucial for the development of autonomous vehicles.\nConvolutional neural networks enable these vehicles to detect and recognize objects on the road, such as other vehicles, pedestrians, and traffic signs.\nThis is essential for safe and reliable navigation, as the vehicle must accurately interpret its surroundings in real-time.\n- **Path Planning and Navigation**\nNeural networks help autonomous vehicles plan their routes and navigate complex environments.\nBy analyzing real-time data from sensors and cameras, these systems, often utilizing deep reinforcement learning, can make split-second decisions to avoid obstacles and ensure a smooth journey.\nThis involves dynamically adjusting the vehicle’s path based on traffic conditions, road obstacles, and other factors.\n...\nMany modern vehicles are equipped with driver assistance systems powered by deep learning.\nIf the driver deviates from a lane, the deep learning algorithm detects it and provides a warning.\nUnlike traditional cruise control where the speed is unalterable, the Adaptive Cruise Control, a feature powered by deep learning, can modify the speed according to traffic and road conditions.\nIt automatically adjusts the speed to maintain a safe distance from the vehicle ahead.\nAutomatic emergency braking is another feature made possible through deep learning.\nBy processing data from cameras and sensors, neural networks can detect potential hazards and assist the driver in avoiding accidents, enhancing safety and convenience.\n### Deep Learning Applications in the Entertainment Industry\n- **Content Recommendation**\nStreaming services like Netflix and Spotify use deep learning to recommend content to users based on their viewing or listening history and preferences.\nRecurrent neural networks analyze user behavior to suggest movies, TV shows, music, and other content, providing a personalized entertainment experience.\nThis increases user engagement and satisfaction.\n- **Video and Image Enhancement**\nDeep learning algorithms enhance the quality of video and images through techniques such as super-resolution and denoising.\nFor example, GANs can upscale low-resolution videos to higher resolutions, remove noise from images, and even colorize black-and-white footage.\nThis improves the visual experience for users and extends the usability of older content.\n- **Game Development**\nIn game development, deep learning is used to create more realistic and intelligent non-player characters (NPCs).\nThese characters, powered by reinforcement learning algorithms, can learn from player behavior and adapt their actions accordingly, providing a more engaging and challenging gaming experience.\nAdditionally, neural networks are used to generate realistic graphics and procedural content, enhancing the overall quality of games.\n...\nThe applications of deep learning and neural networks are vast and continue to expand across industries.\nThese technologies are driving innovation, improving efficiency, and enhancing the quality of products and services.",
"title": "Deep Learning and Neural Networks Applications Across ...",
"url": "https://payodatechnologyinc.medium.com/deep-learning-and-neural-networks-applications-across-industries-c955ce0a8b88",
"date": "2024-09-30",
"last_updated": "2025-09-02"
},
{
"snippet": "",
"title": "Deep Learning Applications & Solutions Across Industries",
"url": "https://scopicsoftware.com/blog/deep-learning-applications-solutions-across-industries/",
"date": "2026-03-30",
"last_updated": "2026-05-16"
},
{
"snippet": "Now, a specific breakthrough in AI — deep learning — is allowing business to use data to teach computers how to learn.\n...\nDeep learning requires vast amounts of data and a huge amount of processing power.\nBut, since industries across the spectrum are generating tons of “Big Data,” i.e. digital data now being produced at an unprecedented rate and massive volumes, there is a clear opportunity for deep learning-powered applications.\nHere are 13 industries that are drawing on innovations pioneered by deep learning to make major advances.\n#### 1.\nHealthcare/genomics\n...\nThis is where deep learning comes in.\nStartup Deep Genomics, which is backed by Bloomberg Beta and True Ventures among others, has fed deep learning machines tons of existing cellular information in order to teach machines to predict outcomes from alterations to the genome, whether naturally occurring or through medical treatment.\nThe technology could provide the most precise understanding of an individual’s specific disease or abnormality and how that person’s wellbeing can best be advanced.\n...\nIf deep learning techniques could look at the wide variety of molecular compounds that have already proven themselves effective and use this information to develop drugs to attack new or established diseases — or identify alternative uses for drugs that are already FDA-approved, it could help fast-track treatments.\n...\nCompanies like Israel-based and Blumberg Capital-backed Deep Instinct aim to use deep learning in order to recognize new threats that have never been detected before and thus keep organizations one step ahead of cyber criminals.\n...\nAmong the oldest and most unpredictable industries, agriculture is becoming increasingly data- and tech-driven.\nMore predictable crop outputs — based on weather forecasting and data-based estimates — could potentially even take some of the uncertainty out of commodities markets.\nNew Mexico-based Descartes Labs aims to make “a living map of all the world’s agriculture” using satellite imagery fed into deep learning machines and lots of computing power, rather than a more traditional focus on surveys and site visits.\nThen, based on patterns over time, the technology is designed to predict crop yields and production.\n...\nRetail/e-commerce\nPinterest — where people often share aspirational images of products — is arguably the leading site for social commerce.\nBut often, these images show a collection of items and don’t necessarily identify where something similar might be purchased.\nNow, using deep learning, the company is turning that repository of images into potential sales by allowing users to zoom in on specific items within an image and be served visually similar pins, some of which could be purchased from retailers’ pins.\nPinterest is using the same deep learning-based discovery technology to surface more relevant videos.\nOther internet publishers might eventually be able to turn every image or video on their site into a kind of high-gloss ad referral.\nThe French company Deepomatic has already used deep learning to develop this kind of tech aimed at web publishers, allowing them to earn revenue off of the visuals within their content.\n...\nThis is why, using deep learning, Drive.ai plans to help the car build up experience through simulations of many kinds of driving conditions.\n...\nNvidia says it has used deep learning to train a car to drive on marked and unmarked roads and along the highway in various weather conditions, without the need to program every possible “if, then, else” statement.\n#### 7.\n...\nSimilarly IBM is using deep learning to help solar and wind companies better predict weather and improve alternative energy production.\n...\nThe insurance industry is beginning to see deep learning as especially useful in managing claims.\nA machine that can recognize patterns in fraud could help these companies ferret out false claims and determine payouts for legitimate claims.\n...\nThe company now sees deep learning as potentially playing a role in claims processing and customer service.\nPwC similarly sees deep learning playing a role in standardized underwriting of common policies, such as auto, home, and commercial insurance, and in using deep learning image analysis to estimate repair costs after a claim has been filed.\nThe startup Tractable is specifically focused on building deep learning systems for auto insurance.\nThe company aims to use images of car damages to teach machines how to estimate future repair costs, potentially bypassing the need to visit the auto body shop and rely on the garage’s assessment.\n...\nNow, a few different companies are using deep learning to help clients — especially enterprise-focused companies — reach out to the right prospects.\n...\nNow it looks like those robots could get even better at their job with the addition of deep learning tech.",
"title": "Get Smart: 13 Industries Using Deep Learning To Make Huge Leaps Forward",
"url": "https://www.cbinsights.com/research/industries-disrupted-deep-learning/",
"date": null,
"last_updated": "2024-06-22"
},
{
"snippet": "",
"title": "Top 50 Deep Learning Use Case & Case Studies - AIMultiple",
"url": "https://aimultiple.com/deep-learning-applications",
"date": "2026-03-10",
"last_updated": "2026-05-25"
},
{
"snippet": "Deep learning, a subset of machine learning, has emerged as a powerful technology with transformative applications across various industries.\nIts ability to mimic the human brain's neural networks enables machines to learn from vast datasets and make intelligent decisions.\nIn this article, we explore how deep learning is applied in diverse sectors, revolutionizing processes and driving innovation.\n**Healthcare:** Deep learning is making significant strides in healthcare, aiding in medical image analysis, disease diagnosis, and personalized treatment plans.\nRadiology, pathology, and drug discovery are areas where deep learning is enhancing accuracy and efficiency.\n**Finance:** In the financial sector, deep learning is employed for fraud detection, risk assessment, and algorithmic trading.\nIts ability to analyze patterns and detect anomalies contributes to strengthening security measures and optimizing financial operations.\n**Manufacturing:** Deep learning is optimizing manufacturing processes through predictive maintenance, quality control, and supply chain management.\nPredictive analytics powered by deep learning helps prevent equipment failures and minimize downtime.\n**Retail:** The retail industry leverages deep learning for customer personalization, demand forecasting, and inventory management.\nRecommendation systems based on deep learning algorithms enhance the customer shopping experience.\n**Automotive:** Autonomous vehicles rely on deep learning for image recognition, object detection, and decision-making processes.\nDeep learning algorithms enable vehicles to interpret and respond to their surroundings, ensuring safer and more efficient transportation.\n**Agriculture:** Precision agriculture benefits from deep learning applications in crop monitoring, pest detection, and yield prediction.\nDeep learning contributes to optimizing farming practices and maximizing agricultural output.\n**Education:** Deep learning is transforming education through personalized learning experiences, adaptive assessment systems, and intelligent tutoring.\nTailoring education to individual needs enhances student engagement and comprehension.\n**Entertainment:** Content recommendation platforms in the entertainment industry utilize deep learning to understand user preferences and provide personalized content suggestions.\nDeep learning algorithms enhance the creation and curation of entertainment content.\n**Telecommunications:** Deep learning plays a role in network optimization, predictive maintenance, and customer service within the telecommunications sector.\nThese applications contribute to improving network performance and customer satisfaction.\n**Energy:** Deep learning aids the energy sector in predictive maintenance of equipment, energy grid optimization, and fault detection.\nThese applications enhance the efficiency and reliability of energy production and distribution.\nIn conclusion, the widespread applications of deep learning underscore its transformative impact on various industries.",
"title": "How Deep Learning is Applied in Various Industries",
"url": "https://www.analyticsinsight.net/deep-learning/how-deep-learning-is-applied-in-various-industries-2",
"date": "2023-11-12",
"last_updated": "2025-07-10"
}
],
"server_time": null
}
Advanced Async Patterns
Rate-Limited Concurrent Processing
For large-scale applications, implement controlled concurrency with rate limiting:import asyncio
from perplexity import AsyncPerplexity
class SearchManager:
def __init__(self, max_concurrent=5, delay_between_batches=1.0):
self.max_concurrent = max_concurrent
self.delay_between_batches = delay_between_batches
self.semaphore = asyncio.Semaphore(max_concurrent)
async def search_single(self, client, query):
async with self.semaphore:
return await client.search.create(query=query, max_results=5)
async def search_many(self, queries):
async with AsyncPerplexity() as client:
tasks = [
self.search_single(client, query)
for query in queries
]
results = await asyncio.gather(*tasks, return_exceptions=True)
# Filter out exceptions and return successful results
successful_results = [
result for result in results
if not isinstance(result, Exception)
]
return successful_results
# Usage
async def main():
manager = SearchManager(max_concurrent=3)
queries = [
"AI research 2024",
"quantum computing advances",
"renewable energy innovations",
"biotechnology breakthroughs",
"space exploration updates"
]
results = await manager.search_many(queries)
print(f"Successfully processed {len(results)} out of {len(queries)} searches")
asyncio.run(main())
import Perplexity from '@perplexity-ai/perplexity_ai';
class SearchManager {
private maxConcurrent: number;
private delayBetweenBatches: number;
constructor(maxConcurrent: number = 5, delayBetweenBatches: number = 1000) {
this.maxConcurrent = maxConcurrent;
this.delayBetweenBatches = delayBetweenBatches;
}
async searchMany(queries: string[]) {
const client = new Perplexity();
const results = [];
// Process in batches to respect rate limits
for (let i = 0; i < queries.length; i += this.maxConcurrent) {
const batch = queries.slice(i, i + this.maxConcurrent);
const batchPromises = batch.map(query =>
client.search.create({ query, max_results: 5 })
.catch(error => ({ error, query }))
);
const batchResults = await Promise.all(batchPromises);
// Filter out errors and collect successful results
const successfulResults = batchResults.filter(
result => !('error' in result)
);
results.push(...successfulResults);
// Add delay between batches
if (i + this.maxConcurrent < queries.length) {
await new Promise(resolve =>
setTimeout(resolve, this.delayBetweenBatches)
);
}
}
return results;
}
}
// Usage
async function main() {
const manager = new SearchManager(3, 1000);
const queries = [
"AI research 2024",
"quantum computing advances",
"renewable energy innovations",
"biotechnology breakthroughs",
"space exploration updates"
];
const results = await manager.searchMany(queries);
console.log(`Successfully processed ${results.length} out of ${queries.length} searches`);
}
main();
Error Handling in Async Operations
Implement robust error handling for async search operations:import asyncio
import logging
from perplexity import AsyncPerplexity, APIStatusError, RateLimitError
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger(__name__)
async def resilient_search(client, query, max_retries=3):
for attempt in range(max_retries):
try:
result = await client.search.create(query=query, max_results=5)
logger.info(f"Search successful for: {query}")
return result
except RateLimitError as e:
if attempt < max_retries - 1:
delay = 2 ** attempt
logger.warning(f"Rate limited for '{query}', retrying in {delay}s")
await asyncio.sleep(delay)
else:
logger.error(f"Max retries exceeded for: {query}")
return None
except APIStatusError as e:
logger.error(f"API error for '{query}': {e}")
return None
except Exception as e:
logger.error(f"Unexpected error for '{query}': {e}")
return None
async def main():
async with AsyncPerplexity() as client:
queries = ["AI developments", "invalid query", "tech trends"]
tasks = [resilient_search(client, query) for query in queries]
results = await asyncio.gather(*tasks)
successful_results = [r for r in results if r is not None]
print(f"Successful searches: {len(successful_results)}/{len(queries)}")
asyncio.run(main())
import Perplexity from '@perplexity-ai/perplexity_ai';
async function resilientSearch(
client: Perplexity,
query: string,
maxRetries: number = 3
) {
for (let attempt = 0; attempt < maxRetries; attempt++) {
try {
const result = await client.search.create({ query, max_results: 5 });
console.log(`Search successful for: ${query}`);
return result;
} catch (error: any) {
if (error.constructor.name === 'RateLimitError') {
if (attempt < maxRetries - 1) {
const delay = 2 ** attempt * 1000;
console.warn(`Rate limited for '${query}', retrying in ${delay}ms`);
await new Promise(resolve => setTimeout(resolve, delay));
} else {
console.error(`Max retries exceeded for: ${query}`);
return null;
}
} else {
console.error(`Error for '${query}':`, error.message);
return null;
}
}
}
return null;
}
async function main() {
const client = new Perplexity();
const queries = ["AI developments", "invalid query", "tech trends"];
const tasks = queries.map(query => resilientSearch(client, query));
const results = await Promise.all(tasks);
const successfulResults = results.filter(r => r !== null);
console.log(`Successful searches: ${successfulResults.length}/${queries.length}`);
}
main();
Performance Optimization Tips
1
Optimize result count
Request only the number of results you actually need. More results = longer response times.
# Good: Request only what you need
search = client.search.create(query="AWS re:Invent generative AI announcements", max_results=5)
# Avoid: Over-requesting results
search = client.search.create(query="AWS re:Invent generative AI announcements", max_results=50)
Response
Response
{
"id": "5ef341de-a9cc-48fe-a4e7-37c8b2d03c4f",
"results": [
{
"snippet": "## Amazon Redshift introduces AWS Graviton-based RG instances with an integrated data lake query engine\n...\nAmazon Redshift RG instances, powered by AWS Graviton, run data warehouse and data lake workloads up to 2.4x as fast as RA3 instances at 30% lower price per vCPU.\nIts integrated data lake query engine supports open table formats such as Apache Iceberg.\n## AWS Weekly Roundup: Claude Mythos Preview in Amazon Bedrock, AWS Agent Registry, and more (April 13, 2026)\n...\n## AWS Weekly Roundup: AWS DevOps Agent & Security Agent GA, Product Lifecycle updates, and more (April 6, 2026)\n...\n## Happy New Year!\nAWS Weekly Roundup: 10,000 AIdeas Competition, Amazon EC2, Amazon ECS Managed Instances and more (January 5, 2026)\n...\n## Amazon Bedrock AgentCore adds quality evaluations and policy controls for deploying trusted AI agents\nby on 02 DEC 2025 in\nDeploy AI agents with confidence using new quality evaluations and policy controls—enabling precise boundaries on agent actions, continuous quality monitoring, and experience-based learning while maintaining natural conversation flows.\n## Amazon S3 Vectors now generally available with increased scale and performance\nby on 02 DEC 2025 in\nScale vector storage and querying to new heights with S3 Vectors’ general availability—now supporting up to 1 billion vectors per index, 100ms query latencies, and expanded regional availability, while reducing costs up to 90% compared to specialized databases.\n## Introducing Amazon Nova 2 Lite, a fast, cost-effective reasoning model\nby on 02 DEC 2025 in\nNew fast, cost-effective model supports extended thinking with adjustable reasoning depth, letting you control the balance between speed, intelligence, and cost while building AI applications for everyday workloads.\n## Introducing Amazon Nova Forge: Build your own frontier models using Nova\nby on 02 DEC 2025 in\nNew program gives organizations unprecedented access to Nova model training, enabling them to build custom frontier models that deeply embed domain expertise without the traditional barriers of cost, compute, and time.",
"title": "Generative AI | AWS News Blog",
"url": "https://aws.amazon.com/blogs/aws/category/artificial-intelligence/generative-ai/",
"date": "2026-05-12",
"last_updated": "2026-05-13"
},
{
"snippet": "Today, Amazon SageMaker AI introduces OpenAI-compatible API support for real-time inference endpoints.\nIf you use the OpenAI SDK, LangChain, or Strands Agents, you can now invoke models on SageMaker AI by changing only your endpoint URL.\n...\nToday, we’re announcing three new capabilities available in SageMaker Python SDK v3.8.0.\n...\nToday, we’re excited to announce the general availability of Claude Platform on AWS.\nClaude Platform on AWS is a new service that gives customers direct access to Anthropic’s native Claude Platform experience through their AWS account, with no separate credentials, contracts, or billing relationships required.\nAWS is the first cloud provider to offer access to the native Claude Platform experience.\n...\nToday, we’re announcing a preview of Amazon Bedrock AgentCore Payments, a new set of features in Amazon Bedrock AgentCore that enables AI agents to instantly access and pay for what they use.\n...\nToday, we’re excited to announce that Amazon SageMaker AI MLflow Apps now support MLflow version 3.10, bringing enhanced capabilities for generative AI development and streamlined experiment tracking to your generative AI workflows.",
"title": "Announcements | Artificial Intelligence - AWS",
"url": "https://aws.amazon.com/blogs/machine-learning/category/post-types/announcements/",
"date": "2026-05-07",
"last_updated": "2026-05-21"
},
{
"snippet": "",
"title": "AWS re:Invent 2024 - Generative AI in action - YouTube",
"url": "https://www.youtube.com/watch?v=aFQFiVOh3P0",
"date": "2024-12-04",
"last_updated": "2026-03-05"
},
{
"snippet": "And although generative AI has appeared in previous events, this year we’re taking it to the next level.\nIn addition to several exciting announcements during keynotes, most of the sessions in our track will feature generative AI in one form or another, so we can truly call our track “Generative AI and ML.”",
"title": "AWS re:Invent | Artificial Intelligence",
"url": "https://aws.amazon.com/blogs/machine-learning/category/events/reinvent/",
"date": "2025-11-17",
"last_updated": "2026-05-03"
},
{
"snippet": "We’ve rounded up the most significant announcements from AWS re:Invent 2025, held Nov. 30-Dec. 4 in Las Vegas.\nThis guide highlights the innovations that will help you build, scale, and transform your business in the cloud.\n...\n(This post was updated 12:57 p.m. PST, Dec. 5, 2025.)\n...\n**AWS Clean Rooms launches privacy-enhancing dataset generation for ML model training**\nTrain ML models on sensitive collaborative data by generating synthetic datasets that preserve statistical patterns while protecting individual privacy through configurable noise levels and protection against re-identification.\n...\n**Introducing Amazon Nova 2 Sonic: Our new speech-to-speech model for conversational AI]\n** Build more natural voice interactions with enhanced speech-to-speech AI—now featuring multilingual conversations, dynamic speech control, crossmodal inputs, and improved telephony integration while maintaining conversation context across tasks.\n**Introducing Amazon Nova 2 Lite, a fast, cost-effective reasoning model\n** Nova 2 Lite offers fast, cost-effective AI for everyday tasks.\nIt features extended thinking capabilities, built-in tools, and a million-token context window.\n**Introducing Amazon Nova Forge: Build your own frontier models using Nova\n** New program gives organizations unprecedented access to Nova model training, so they can build custom frontier models that deeply embed domain expertise without the traditional barriers of cost, compute, and time.\n**Introducing Amazon Nova 2 Omni in Preview**\nOur all-in-one model for multimodal reasoning and image generation supports text, images, video, and speech inputs while generating both text and image outputs.\n**Build reliable AI agents for UI workflow automation with Amazon Nova Act, now generally available\n** New AWS service helps developers build AI agents that automate browser-based tasks like form filling, search & extract, shopping & booking, and QA testing—with over 90% reliability for enterprise deployments.\n**Amazon Bedrock AgentCore adds quality evaluations and policy controls for deploying trusted AI agents**\nNew capabilities help companies deploy agents with enhanced policy controls, quality monitoring, improved memory, and natural conversation abilities—making it easier to confidently scale AI solutions across your organization.\n**Amazon S3 Vectors now generally available with increased scale and performance**\nScale vector storage and querying to new heights with S3 Vectors’ general availability—now supporting up to 2 billion vectors per index, 100ms query latencies, and expanded regional availability, while reducing costs up to 90% compared to specialized databases.\n**Amazon Bedrock adds 18 fully managed open weight models, including the new Mistral Large 3 and Ministral 3 models**\nAccess fully managed foundation models from leading providers like Google, Kimi AI, MiniMax AI, Mistral AI, NVIDIA, OpenAI, and Qwen, including the new Mistral Large 3 and Ministral 3 3B, 8B, and 14B models through Amazon Bedrock.\n**Accelerate AI development using Amazon SageMaker AI with serverless MLflow**\nSimplify AI experimentation with zero-infrastructure MLflow that launches in minutes, scales automatically, and seamlessly integrates with SageMaker’s model customization and pipeline capabilities.\n**Amazon Bedrock adds reinforcement fine-tuning simplifying how developers build smarter, more accurate AI models**\nNew model customization capability uses feedback-driven training to deliver 66% accuracy gains over base models without requiring large labeled datasets or deep ML expertise.\n**Introducing checkpointless and elastic training on Amazon SageMaker HyperPod**\nAccelerate AI model development with new training features that enable instant recovery from failures and automatic scaling based on resource availability.\n...\n**AWS introduces Graviton5—the company’s most powerful and efficient CPU**\n...\n**Introducing AWS AI Factories**\nDeploy fully managed AWS AI infrastructure in your data centers with pre-integrated foundation models, specialized hardware, and AWS services—helping enterprises and governments accelerate AI initiatives while meeting data residency requirements.\n...\n**Amazon FSx for NetApp ONTAP now integrates with Amazon S3 for seamless data access\n** Amazon FSx for NetApp ONTAP now lets you access file system data through Amazon S3, enabling seamless integration with AWS analytics, ML, and generative AI services without moving or copying data.",
"title": "Top announcements of AWS re:Invent 2025 | AWS News Blog",
"url": "https://aws.amazon.com/blogs/aws/top-announcements-of-aws-reinvent-2025/",
"date": "2025-11-30",
"last_updated": "2026-05-18"
},
{
"snippet": "",
"title": "AWS re:Invent | Watch on demand",
"url": "https://aws.amazon.com/events/reinvent/on-demand/",
"date": "2026-05-13",
"last_updated": "2026-05-21"
},
{
"snippet": "",
"title": "Your guide to generative AI and ML at AWS re:Invent 2024",
"url": "https://aws.amazon.com/blogs/machine-learning/your-guide-to-generative-ai-and-ml-at-aws-reinvent-2024/",
"date": "2024-11-19",
"last_updated": "2026-05-17"
},
{
"snippet": "",
"title": "AWS re:Invent 2024 Highlights: Top takeaways from Swami ...",
"url": "https://aws.amazon.com/blogs/machine-learning/aws-reinvent-2024-highlights-top-takeaways-from-swami-sivasubramanian-to-help-customers-manage-generative-ai-at-scale/",
"date": "2024-12-16",
"last_updated": "2026-04-10"
},
{
"snippet": "News from AWS re:Invent, including all things generative AI, new service announcements, tech demos, and more.\nGenerative artificial intelligence (Gen AI) was again a major focus, with Amazon Web Services (AWS) CEO Matt Garman, AWS Vice President of Data and AI Swami Sivasubramanian, and special guest Amazon CEO Andy Jassy announcing a range of innovations, as well as sharing behind-the-scenes-insights.Here are some of the key announcements from the event:\n## 1.\nAmazon Nova foundation models\nNew Amazon Nova models, available in Amazon Bedrock, can process text, image, and video as prompts.\nCustomers can use Amazon Nova-powered Gen AI applications to understand videos, charts, and documents, or generate multimedia content.\nUsing Amazon Bedrock, customers can easily experiment with and evaluate Amazon Nova models, as well as other foundation models (FMs), to determine the best model for an application.\n## 2.\nNow available: AWS Trainium2 instances\nAWS Trainium2-powered Amazon Elastic Compute Cloud (Amazon EC2)instances, powered by AWS Trainium2 chips, are purpose-built for high-performance deep learning (DL) training of Gen AI models, including large language models (LLMs) and latent diffusion models.\nA single Trn2 instance combines 16 Trainium2 chips interconnected with ultrafast NeuronLink high-bandwidth.Trn2 UltraServers—a completely new EC2 offering—use NeuronLink interconnect to connect four Trn2 servers together into one giant server.\nWith new Trn2 UltraServers, customers can scale up their generative AI workloads across 64 Trainium2 chips.\n...\n## 3.\nTrainium3 chips: designed for high-performance needs of Gen AI workloads\nTrainium3, AWS’s next-generation AI chip, will allow customers to build bigger models faster, and deliver superior real-time performance.\nTrainium3-powered UltraServers are expected to be four times more performant than Trn2 UltraServers.\nThe first Trainium3-based instances will be available in late 2025.\n## 4.\nNew capabilities for Amazon Bedrock and the biggest expansion of models to date\nNew innovations for Amazon Bedrock will give customers greater flexibility and control to build and deploy production-ready Gen AI faster.\nThese include:- The broadest selection of fully managed models from leading AI companies\n- Access to more than 100 popular, emerging, and specialized models with Amazon Bedrock Marketplace\n- New Amazon Bedrock capabilities to help customers more effectively manage prompts at scale\n- Two new capabilities for Amazon Bedrock Knowledge Bases\n- New Amazon Bedrock Data Automation\n## 5.\nAmazon Bedrock strengthened with industry-first AI safeguard, new agent capability, and model customization\nNew capabilities for Amazon Bedrock will help customers prevent factual errors due to hallucinations, orchestrate multiple AI-powered agents for complex tasks, and create smaller, task-specific models that can perform similarly to a large model at a fraction of the cost and latency:- Automated reasoning checks.\nThe first and only Gen AI safeguard that helps prevent factual errors due to hallucinations using logically accurate and verifiable reasoning.\n- Expansion of Amazon Bedrock Agents.\nSupports multi-agent collaboration, empowering customers to easily build and coordinate specialized agents to execute complex workflows.\n- Amazon Bedrock Model Distillation.\nModel distillation is a technique that transfers the knowledge from a large model to a small model, while retaining the latter’s performance characteristics.\nThis usually requires specialized machine learning (ML) expertise, but with Amazon Bedrock Model Distillation, any customer can now distill their own model with no ML expertise required.\n...\n## 6.\nNew Amazon SageMaker AI capabilities\nNew innovations for Amazon SageMaker AI can help customers get started faster with popular publicly available models, maximize training efficiency, lower costs, and use their preferred tools to accelerate Gen AI model development.\nThese include three innovations for Amazon SageMaker HyperPod, which helps customers efficiently scale Gen AI model development across thousands of AI accelerators, reducing time to train foundation models by up to 40%.\n## 7.\nNext generation of Amazon SageMaker to deliver unified platform for data, analytics, and AI\nThe next generation of Amazon Sagemaker now showcases four new features and capabilities: SageMaker Unified Studio, SageMaker Catalog, SageMaker Lakehouse, and Zero-ETL integrations with SaaS applications.\nThis brings together the capabilities customers need for fast Structured Query Language (SQL) analytics, petabyte-scale big data processing, data exploration and integration, machine learning (ML) model development and training, and Gen AI in one integrated platform.\n## 8.\nNew capabilities and continued momentum for Amazon Q Business\nAmazon Q Business is the most capable Gen AI-powered assistant for finding information, gaining insight, and taking action at work.\nThe new capabilities offer customers better insights across Amazon Q Business and Amazon Q in QuickSight, enhance cross-app Gen AI experiences, provide more than 50 actions for popular business applications, and make it easy to automate complex workflows—enabling employees to complete tedious, time-consuming work faster.\n## 9.\nNew enhancements to Amazon Q Developer, the most capable Gen AI assistant for software development\nThese enhancements to Amazon Q Developer include agents that accelerate unit testing, documentation, and code reviews, and an operational capability that helps operators and developers of all experience levels investigate and resolve operational issues across their AWS environment in a fraction of the time.\nAmazon Q Developer can speed up software development tasks by up to 80%, already providing the highest reported code acceptance rate of any coding assistant that suggests multi-line code; code security scanning that outperforms leading publicly benchmarkable tools; and high-performing AI agents that autonomously reason and iterate to achieve complex goals.\n...\n## 10.\nNew database capabilities including Amazon Aurora DSQL, the fastest distributed SQL database\nNew capabilities for Amazon Aurora and Amazon DynamoDB will support customers’ most demanding workloads that need to operate across multiple Regions with strong consistency, low latency, and the highest availability:- Amazon Aurora DSQL.\nhis new serverless, distributed SQL database enables customers to build applications with 99.999% multi-Region availability, strong consistency, PostgreSQL compatibility, four times faster reads and writes compared to other popular distributed SQL databases, virtually unlimited scalability, and zero infrastructure management.\n- Enhancements to Amazon DynamoDB global tables.\nAWS is now using the same underlying technology leveraged by Aurora DSQL to enhance DynamoDB global tables, adding the option of strong consistency to the highest availability, virtually unlimited scalability, and zero infrastructure management already available in DynamoDB global tables, ensuring customers' multi-Region applications are always reading the latest data without having to change any application code.\n## 11.\nNew data center components to support AI, improve energy efficiency, and boost innovation\nAWS expects to begin construction on new data centers with the full set of new components—which combine advances in power, cooling, and hardware—in early 2025 in the U.S. Watch this video and learn more about AWS's new data center components.",
"title": "11 key announcements from AWS re:Invent 2024 - About Amazon",
"url": "https://www.aboutamazon.com/news/aws/amazon-nova-ai-canvas-reel-aws-reinvent",
"date": "2024-12-06",
"last_updated": "2026-05-21"
},
{
"snippet": "This post is your retroactive field guide to connecting the dots that define the future of the cloud.\nBelow, we'll dive into the biggest announcements from three crucial domains:\n- Applied intelligence, and the promise of agentic AI\n- Trust, and the necessary governance and security to scale safely\n- Enterprise velocity, and keeping pace with the agentic era\n...\nIf one thing was clear in re:Invent’s opening keynote, it’s that builders have more capability than ever to train models, build agents, and provide value by using technologies like AWS Trainium 3 and Amazon Bedrock AgentCore.\n...\nLast year at DASH, we launched LLM Observability, a way to continuously observe, secure, and act on findings from the LLMs you use.\nJust in time for re:Invent, we also launched two features in LLM Observability to help builders see the full picture of their stack: support for Strands Agents and for Amazon Bedrock Agents, both generally available.\nIn order to ensure both safety and security, we released a set of detection rules to help drive down risk in the configuration for Bedrock instances.\nThe AWS commitment to empowering their customers and partners to build safely on their AI platform was also echoed in the release of Bedrock AgentCore Policies, enabling teams to have fine-grained control of identity and access throughout their agentic workflows.\n...\nWhen it comes to AI, builders now have more choice than ever.\nAWS dropped 18 fully managed open weight models for Bedrock—the largest simultaneous model expansion to date—spanning Gemma, Mistral, Qwen, NVIDIA Nemotron, and more.\nPair that with the new serverless model customization capability in SageMaker AI, which lets teams fine-tune with reinforcement learning and direct preference optimization without managing infrastructure, and suddenly the number of variables explodes.\nThis raises a new challenge: How do I tune all of these models for the best results?\nLLM Observability now includes LLM Experiments and Playground, where you can experiment and optimize your LLM applications before pushing to production.\nIn addition to model tuning, we released Prompt Tracking to help monitor performance, latency, and drift across prompt versions.\n...\nBut before that, two major announcements came in the days leading up to re:Invent—or as we call it, pre:Invent:\n- AWS IAM Policy Autopilot: An open source Model Context Protocol (MCP) server and command-line tool that helps your AI coding assistants quickly create baseline IAM policies that you can refine as your application evolves.\nPolicy Autopilot supports all the major languages and IDEs, including Kiro.\n- AWS Security Agent: Launched in preview, this frontier agent is a step toward integrating more agentic application security into build workflows.\nApplied AI and triaging as many findings close to the IDE will certainly save AppSec teams a ton of time.\n...\nWe launched two capabilities that help you understand your larger cloud footprint and prioritize risks using observability context.\n...\nA wave of re:Invent announcements centered on Kiro, AWS's agentic IDE.\nKiro focuses on moving from prototype to production, with AI agents that understand context across large codebases.\nWe launched three integrations to extend Kiro's reach into production, bringing errors, deployments, traces, and incident context directly into the code editor:\n- Datadog MCP Server support in Kiro surfaces errors, deployments, and traces directly in your editor, shortening the feedback loop between code and production.\n...\nAt re:Invent, we released new AI capabilities for Code Security to help developers catch vulnerabilities and fix them without slowing down.\nAI-driven detection and remediation detects code vulnerabilities, intelligently filters out false positives, and helps developers remediate at scale, while Secret Scanning detects and blocks exposed credentials leaked in code.\nWith your code secured, the next question is: How fast can you get it to production?\nAWS announced several capabilities that remove friction across the deployment life cycle:\n- Amazon ECS Express Mode lets you deploy containerized applications with a single command—load balancers, autoscaling, networking, and domains are provisioned automatically.\n- AWS Lambda durable functions bring automatic checkpointing and retries to long-running workflows, with execution that can pause for up to a year and resume exactly where it left off.\n- AWS Lambda Managed Instances removes the tradeoff between serverless simplicity and Amazon EC2 flexibility, allowing you to run Lambda functions on EC2 compute while AWS handles instance life cycle, patching, and scaling.",
"title": "Highlights from AWS re:Invent 2025: Making sense of applied AI ...",
"url": "https://www.datadoghq.com/blog/aws-reinvent-2025-recap/",
"date": "2025-12-11",
"last_updated": "2026-05-17"
}
],
"server_time": null
}
2
Cache frequently used searches
Implement caching for queries that don’t need real-time results.
import time
from typing import Dict, Tuple, Optional
class SearchCache:
def __init__(self, ttl_seconds=3600): # 1 hour default
self.cache: Dict[str, Tuple[any, float]] = {}
self.ttl = ttl_seconds
def get(self, query: str) -> Optional[any]:
if query in self.cache:
result, timestamp = self.cache[query]
if time.time() - timestamp < self.ttl:
return result
else:
del self.cache[query]
return None
def set(self, query: str, result: any):
self.cache[query] = (result, time.time())
# Usage
cache = SearchCache(ttl_seconds=1800) # 30 minutes
def cached_search(client, query):
cached_result = cache.get(query)
if cached_result:
return cached_result
result = client.search.create(query=query)
cache.set(query, result)
return result
class SearchCache {
private cache: Map<string, { result: any; timestamp: number }> = new Map();
private ttl: number;
constructor(ttlSeconds: number = 3600) { // 1 hour default
this.ttl = ttlSeconds * 1000; // Convert to milliseconds
}
get(query: string): any | null {
const cached = this.cache.get(query);
if (cached) {
if (Date.now() - cached.timestamp < this.ttl) {
return cached.result;
} else {
this.cache.delete(query);
}
}
return null;
}
set(query: string, result: any): void {
this.cache.set(query, { result, timestamp: Date.now() });
}
}
// Usage
const cache = new SearchCache(1800); // 30 minutes
async function cachedSearch(client: Perplexity, query: string) {
const cachedResult = cache.get(query);
if (cachedResult) {
return cachedResult;
}
const result = await client.search.create({ query });
cache.set(query, result);
return result;
}
Related Resources
Quickstart
Get started with basic search functionality
Perplexity SDK
Explore the full SDK capabilities for enhanced performance
API Reference
Complete Search API documentation