Another Crazy Day in AI 65/110

Another Crazy Day in AI: Tired of Endless Tabs? Let ChatGPT Do the Research

Feb 3, 2025
3 min read

Hello, AI Enthusiasts.

How did your Monday go? As we transition into the week, there are some noteworthy updates in the AI realm! 🌙

OpenAI has launched "deep research" in ChatGPT, a revolutionary agent that transforms online research. This feature allows users to tackle complex, multi-step research tasks in just tens of minutes, significantly cutting down on the time traditionally required. ⏱️

In other news, MIT has initiated the Generative AI Impact Consortium, uniting top researchers and industry leaders to investigate AI’s societal impact. Meanwhile, a new large-scale study reveals that humans tend to rate AI-generated ideas as more creative than those produced solely by people.

Here's another crazy day in AI:

Deep research is here—what that means for you
MIT launches AI consortium to bridge research and industry
Study examines how AI enhances and challenges human creativity
Some AI tools to try out

TODAY'S FEATURED ITEM: OpenAI’s New Deep Research Feature

Image Credit:Wowza (created with Ideogram)

Are you tired of spending hours sifting through countless online sources for comprehensive research?

OpenAI has recently rolled out deep research, an exciting new feature in ChatGPT that’s designed to tackle complex, multi-step research tasks on the internet. Rather than spending your valuable time manually searching through endless pages of information, deep research steps in to do the heavy lifting for you. It finds, analyzes, and synthesizes large amounts of data into a well-organized report. While this tool is tailored for professionals in finance, science, policy, and engineering, it also offers substantial value for anyone seeking in-depth, well-cited insights for work or personal use.

What makes deep research different?

Searches across multiple sources and compiles relevant information
Organizes findings into structured reports with citations
Handles layered, complex topics that require deeper analysis
Uses an advanced OpenAI model to process and synthesize data
Available to Pro users, with potential for broader access in the future

Deep research represents a shift in how AI assists with information gathering. Instead of just retrieving individual facts, it compiles relevant data, identifies credible sources, and presents findings in a way that supports deeper understanding. For professionals handling extensive research, this tool has the potential to save time while offering a more structured approach to complex topics.

That said, like any AI-driven tool, deep research isn’t flawless. The quality of its reports depends on the sources it finds, and it may not always capture the full depth of highly specialized subjects. The process also takes time—sometimes up to 30 minutes—because it’s conducting multiple layers of analysis. OpenAI continues refining the feature, working on improving accuracy and broadening the range of accessible sources.

As AI tools become more sophisticated, their role in research is expanding. While they don’t replace critical thinking or expertise, they can serve as powerful assistants, helping users make sense of an overwhelming amount of information. The way we approach research is evolving, and AI is becoming an essential part of that transformation.

Read the full article here.

Watch the full video here.

OTHER INTERESTING AI HIGHLIGHTS:

MIT Launches AI Consortium to Bridge Research and Industry

/Liam McDonnell on MIT News

MIT has launched the Generative AI Impact Consortium, bringing together top researchers and industry leaders to explore AI’s societal impact. With founding members including OpenAI, Coca-Cola, and Tata Group, the initiative aims to shape AI's development while addressing challenges such as ethical considerations and workforce adaptation. Through interdisciplinary research, the consortium seeks to ensure AI’s benefits are maximized while mitigating potential risks.

Read more here.

Study Examines How AI Enhances and Challenges Human Creativity

/Ethan Mollick, X (Twitter)

A new large-scale study suggests that AI-generated ideas are rated more creative by humans than those produced by people alone. While AI-augmented humans perform better than unaided individuals, ChatGPT’s standalone ideas still rank the highest. These findings add to growing evidence that AI is reshaping creative and strategic thinking, highlighting both its potential and the need for effective human-AI collaboration.

Read more here.

Check out the survey here.

SOME AI TOOLS TO TRY OUT:

LLM Hydra - AI forum where expert personas provide diverse insights.
Reap - AI-powered tool that transforms long videos into social-ready shorts.
Rootly - Automates incident response by assembling the right people and tools.

That’s a wrap on today’s Almost Daily craziness.

Catch us almost every day—almost! 😉

EXCITING NEWS:

The Another Crazy Day in AI newsletter is now on LinkedIn!!!

Leveraging AI for Enhanced Content: As part of our commitment to exploring new technologies, we used AI to help curate and refine our newsletters. This enriches our content and keeps us at the forefront of digital innovation, ensuring you stay informed with the latest trends and developments.

Another Crazy Day in AI: Better STEM Reasoning in a Smaller Package

Jan 31, 2025
3 min read

Hello, AI Enthusiasts.

How’s your week wrapping up? Are you looking forward to some weekend relaxation? 🌙

OpenAI has just launched a new model, emphasizing its focus on enhancing cost-effective AI reasoning capabilities. Users can look forward to improved performance in STEM-related tasks, particularly in science, mathematics, and coding applications. 📊

Meanwhile, Google has subtly rolled out its latest AI model, sharing the news in a changelog rather than a big announcement. Plus, check out the guide with practical ways for managers to integrate generative AI into their daily workflows so they can focus on strategy. 📈

Here's another crazy day in AI:

OpenAI’s new model for math, science & coding
Tech world reacts to Google’s subtle Gemini 2.0 Pro launch
How AI can handle routine tasks so managers can focus on strategy
Some AI tools to try out

TODAY'S FEATURED ITEM: OpenAI Just Released The o3-mini

Image Credit:Wowza (created with Ideogram)

Can machines think harder on demand?

OpenAI has just introduced o3-mini, a compact yet powerful AI model designed for fast, cost-efficient reasoning, especially in science, math, and coding. Developed as an upgrade to o1-mini, this new model improves speed, accuracy, and developer flexibility. It includes features like function calling, structured outputs, and adjustable reasoning effort modes—allowing it to optimize performance based on the task. The model is now available in ChatGPT and through OpenAI’s API, with access expanding to more users soon.

Here’s what sets o3-mini apart:

Improved reasoning – Tackles complex math, coding, and scientific inquiries with heightened precision.
Quicker responses – Generates answers 24% faster than its predecessor.
Adjustable reasoning depth – Developers can select low, medium, or high reasoning effort, tailoring the balance of speed and complexity.
Wider accessibility – Free-tier ChatGPT users can now utilize reasoning models via a new “Reason” option in the message composer.
Enhanced search capabilities – Currently being tested for up-to-date responses with source links.
Increased rate limits – ChatGPT Plus and Team users benefit from triple the previous message limit.
Stronger safety measures – Demonstrates fewer critical errors and improved safeguards in testing.

This release signals a shift in AI development—prioritizing efficiency without sacrificing quality. While large-scale models still have their place, o3-mini shows that smaller, more targeted models can offer powerful reasoning without the high computational costs. This makes AI more practical for everyday use, particularly in STEM fields where accuracy and speed are essential.

For users and developers, this means greater flexibility. The ability to adjust reasoning depth allows AI to be tailored to specific tasks, whether it’s solving complex equations, writing clean code, or assisting with technical research. And with faster response times, interactions feel smoother and more efficient.

While o3-mini isn’t designed for tasks like image generation (for that, o1 remains the go-to), it marks another step toward AI models that optimize intelligence rather than just scale in size. As AI continues to evolve, advancements like this push the boundaries of what smaller models can achieve—bringing smarter, more adaptable tools to more people.

Read the full article here.

OTHER INTERESTING AI HIGHLIGHTS:

Tech World Reacts to Google’s Subtle Gemini 2.0 Pro Launch

/Kyle Wiggers on TechCrunch

Google has introduced Gemini 2.0 Pro Experimental, its latest AI model, with little fanfare, revealing it in a changelog instead of a major announcement. The model, available to Gemini Advanced users, promises improvements in factual accuracy, coding, and mathematical tasks. However, Google warns that it’s still in an early preview stage, with potential unexpected behaviors. This quiet launch comes amid increased competition from Chinese AI startup DeepSeek, which has shaken up Silicon Valley and the AI industry.

Read more here.

How AI Can Handle Routine Tasks So Managers Can Focus on Strategy

/Christian Stadler on Forbes

With AI adoption rapidly increasing among managers, this guide highlights practical ways to integrate generative AI into daily workflows. Simple tasks like drafting emails, brainstorming ideas, and automating routine processes can yield immediate efficiency gains. AI can also replace some contractor work, such as drafting contracts or generating marketing content, saving both time and money. By focusing on low-risk, high-impact use cases, managers can leverage AI to streamline their work without requiring extensive training.

Read more here.

Check the survey here.

SOME AI TOOLS TO TRY OUT:

Think Deeper - Enhanced reasoning for all Copilot users.
PaperPilot - AI-powered summaries, smart notes, and podcasts for research papers.
Co.dev - Build and deploy apps with AI-written code that auto-fixes errors.

That’s a wrap on today’s Almost Daily craziness.

Catch us almost every day—almost! 😉

EXCITING NEWS:

The Another Crazy Day in AI newsletter is now on LinkedIn!!!

Another Crazy Day in AI: The Indoor Training Effect

Jan 30, 2025
3 min read

Hello, AI Enthusiasts.

Thursday night AI digest—because the weekend is almost here!

MIT researchers just dropped a surprising finding: AI trained in predictable settings actually thrives in unpredictable real-world situations. 🔬

Meanwhile, OpenAI is teaming up with the Agency Fund to supercharge AI-driven solutions for nonprofits solving global issues. 🌍 And during the Super Bowl, Google is showcasing 50 small businesses—one from each state—leveraging AI to grow. 🏆

Here's another crazy day in AI:

MIT’s surprising discovery about AI training
AI accelerator launches to advance global development solutions
Google Super Bowl ads show how AI supports small business growth
Some AI tools to try out

TODAY'S FEATURED ITEM: Training AI in a Different Way

Image Credit:Wowza (created with Ideogram)

Can training in a quiet, controlled space actually make AI perform better in chaos?

A surprising new study from MIT suggests that AI agents may actually learn better when trained in a more controlled, predictable environment—rather than one that mimics real-world unpredictability. This challenges the common assumption that AI should be trained under conditions that closely resemble where it will eventually be used. The research, featured in an article by Adam Zewe on MIT News, explores what the team calls the indoor training effect. Their experiments showed that AI trained in a stable setting often outperforms AI trained in noisy, unpredictable conditions when tested in those same uncertain environments.

What the researchers found:

AI trained in a controlled environment adapted better to unpredictable situations.
The study used Atari games modified to include randomness, testing how AI handled uncertainty.
Training in a noise-free environment helped AI learn patterns more effectively.
The indoor training effect was consistently observed across different game variations.
These findings could reshape how AI is trained for real-world applications like robotics and automation.

At first glance, this might seem counterintuitive. Conventional wisdom suggests that an AI should be exposed to the same challenges during training that it will face in deployment. But this research suggests that learning in a predictable setting first may give AI a stronger foundation, allowing it to generalize better when faced with uncertainty. It’s similar to how humans learn—a tennis player who first masters techniques in an indoor court may perform better on a windy outdoor court than someone who only ever practiced in unpredictable conditions.

This discovery raises important questions about AI training strategies. If AI can benefit from structured learning before facing real-world challenges, how might this approach be applied to complex fields like autonomous systems, decision-making algorithms, or even AI-driven scientific research? While this study focused on simple reinforcement learning tasks, its implications could extend far beyond that. The next step is to explore how this method applies to more advanced AI systems and whether it can lead to more reliable, adaptable technology in the future.

Read more here.

OTHER INTERESTING AI HIGHLIGHTS:

AI Accelerator Launches to Advance Global Development Solutions

/Han Sheng Chia and Dany Bahar on Center for Global Development

AI is transforming global development, with studies showing its potential to improve education, healthcare, and agriculture. The AI for Global Development Accelerator, led by the Agency Fund in collaboration with OpenAI, aims to scale AI-powered solutions for nonprofits tackling major development challenges. Seven organizations will receive funding and technical support to integrate AI into initiatives spanning early childhood education, maternal health, and small-scale farming. This accelerator seeks to answer critical questions about AI’s cost-effectiveness and impact while ensuring ethical and equitable adoption.

Read more here.

Google Super Bowl Ads Show How AI Supports Small Business Growth

/Molly McHugh-Johnson, Google-The Keyword

During this year’s Super Bowl, Google is spotlighting 50 small businesses across all 50 states that use AI to grow and streamline their operations. These commercials showcase how entrepreneurs—from ranchers to boat makers—are leveraging Google Workspace with Gemini to enhance productivity, marketing, and customer service. The campaign highlights the diverse ways AI is supporting small businesses, helping them focus on their passion while handling daily tasks more efficiently. With nearly 34 million small businesses in the U.S., AI-driven solutions are becoming essential for innovation and growth.

Read more here.

SOME AI TOOLS TO TRY OUT:

Guse - AI-powered writing tool for research, editing, and sharing.
Wepost - Automate content creation, publishing, and analytics for social media.
Nowadays - AI copilot that contacts venues and handles event negotiations.

That’s a wrap on today’s Almost Daily craziness.

Catch us almost every day—almost! 😉

EXCITING NEWS:

The Another Crazy Day in AI newsletter is now on LinkedIn!!!