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Alibaba Joins The Chinese LLM Race Giving OpenAI More To Worry About

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Image Source: “Alibaba Group provisional office at Xiong’an (20180503164635)” by N509FZ is licensed under CC BY-SA 4.0. https://commons.wikimedia.org/w/index.php?curid=68790993

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It seems like Silicon Valley has a new reason to sweat. DeepSeek, a Chinese startup, has been making waves with its incredibly fast and efficient AI models, and now Alibaba, the massive Chinese tech company, is joining the fray.

They just announced a whole bunch of new AI models, including one called Qwen 2.5 Max that they claim is even better than DeepSeek’s and America’s best.

Alibaba is throwing down the gauntlet, saying Qwen 2.5 Max can not only write text, but also create images and videos, and even search the web. They’ve got charts and graphs showing how it supposedly beats out OpenAI’s GPT-4, Anthropic’s Claude, and Meta’s Llama in a bunch of tests.

While it’s always smart to be a bit skeptical of these kinds of claims, if they’re true, it means that the US might not be as far ahead in the AI race as everyone thought.

It’s worth noting that Alibaba is comparing their new model to an older version of DeepSeek’s AI, not the latest and greatest one that has everyone talking. But still, this is a big deal.

It makes you wonder if all the billions of dollars that US companies are pouring into AI development is really necessary, especially when Chinese companies seem to be achieving similar results with less fanfare.

Unfortunately, Alibaba is playing their cards close to their chest. They haven’t revealed much about how Qwen 2.5 Max actually works, and unlike DeepSeek, they’re not letting people download and play with it. All we really know is that it uses a similar approach to DeepSeek, with different parts of the AI specializing in different tasks. This allows them to build bigger models without slowing them down.

Alibaba also hasn’t said how big Qwen 2.5 Max is, but it’s probably pretty massive. They’re offering access to it through their cloud service, but it’s not cheap.

In fact, it’s significantly more expensive than using OpenAI’s models. So while it might be more powerful, it might not be the best choice for everyone.

This new model is just the latest in a long line of AI models from Alibaba. They’ve been steadily releasing new ones, including some that are open source and free to use.

They’ve also got specialized models for things like math and code, and they’re even working on AI that can “think” like OpenAI’s latest models.

Basically, Alibaba is going all-in on AI, and they’re not afraid to show it. This is definitely something to keep an eye on, as it could have a major impact on the future of AI and the balance of power in the tech world.

Despite all the excitement surrounding these Chinese AI models, we can’t ignore some serious concerns about censorship and privacy.

Both DeepSeek and Alibaba are Chinese companies, and their privacy policies state that user data can be stored in China. This might not seem like a big deal to everyone, but it raises red flags for some, especially with growing concerns about how the Chinese government handles data. One OpenAI developer even sarcastically pointed out how willing Americans seem to be to hand over their data to the Chinese Communist Party in exchange for free services.

There are also worries about censorship. It’s likely that these Chinese AI models will be censored on topics that the Chinese government considers sensitive. We’ve already seen this with other Chinese AI models, where they avoid or outright refuse to answer questions about things like the Tiananmen Square protests or Taiwan’s independence.

So, while these advancements in Chinese AI are exciting, we need to be aware of the potential downsides. It’s a trade-off between impressive technology and important values like privacy and freedom of information.

Stanford’s AI Now Writes Reports Like A Seasoned Wikipedia Editor (And That’s Kind Of A Big Deal)

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Image Source: “‘Stanford 2’ Apple Store, Stanford Shopping Center” by Christopher Chan is licensed under CC BY-NC-ND 2.0. https://www.flickr.com/photos/17751217@N00/9704608791

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Ever wished you had a personal researcher who could whip up detailed, Wikipedia-style reports on any topic imaginable? Well, Stanford University might just have made that dream a reality. A team of brainy researchers there has created an AI called “WikiGen” that can churn out comprehensive reports that look and feel like they were written by a seasoned Wikipedia editor.

Now, this isn’t your average chatbot spitting out a few bullet points. WikiGen is different. It was trained on a carefully curated diet of top-notch Wikipedia articles, so it’s learned the art of structuring information, writing in a neutral tone, and sticking to the facts like glue.

The result? WikiGen can generate reports on anything from the history of the Ottoman Empire to the intricacies of quantum physics. And these aren’t just rehashed Wikipedia entries; they’re fresh, synthesized reports that pull together information from various sources and present it in a clear, concise, and engaging way, complete with sections, subsections, and even relevant images. It’s like having a mini-Wikipedia at your fingertips!

Imagine the possibilities! Students struggling with a research paper can get a head start with a WikiGen-generated report. Journalists covering a breaking news story can quickly get up to speed on the background context. Heck, even curious folks like you and me can dive deep into any topic that tickles our fancy.

But with great power comes great responsibility, right? The Stanford team is well aware of the potential ethical pitfalls. What if someone uses WikiGen to generate biased or misleading information? Or tries to pass off AI-generated content as their own? They’re working hard to build safeguards into WikiGen to prevent misuse and ensure transparency. Think of it like giving the AI a strong moral compass.

For example, they are exploring ways to clearly label WikiGen’s output so readers know it was generated by an AI. They are also working on methods to detect and mitigate biases that might creep into the model’s training data. This is an ongoing process, as AI ethics is a complex and evolving field.

The best part? Stanford is planning to release WikiGen as an open-source project. This means that researchers and developers around the world can tinker with it, improve it, and build amazing new applications on top of it.

It’s like giving the keys to a powerful knowledge-creation machine to the global community. This open approach encourages collaboration and accelerates the pace of innovation, allowing WikiGen to evolve and adapt to the needs of users worldwide.

This is a big deal, folks. WikiGen has the potential to change how we access and consume information. It could democratize knowledge, empower students and researchers, and even transform the way news is reported. And this is just the beginning. As AI technology continues to evolve, who knows what other incredible tools and applications will emerge? One thing’s for sure: the future of information is looking brighter and more accessible than ever.

OpenEuroLLM: Europe’s Alternative To Silicon Valley And DeepSeek In The LLM Space

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Image Source: “All roads lead to Silicon Valley” by PeterThoeny is licensed under CC BY-NC-SA 2.0. https://www.flickr.com/photos/98786299@N00/25927533872

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It seems like the AI world is becoming a bit of a battleground! While China’s DeepSeek is shaking things up by challenging the big players in Silicon Valley, a new force is emerging in Europe with a different vision for the future of AI.

Imagine a team of European researchers and companies joining forces to create their own powerful AI, but with a focus on benefiting Europe as a whole.

That’s the idea behind OpenEuroLLM. They’re not just trying to build the biggest and best AI models; they want to use AI to boost European businesses, improve public services, and make the continent a leader in the digital world.

Think of it like a European “AI for good” initiative. They’re building a collection of advanced language models that can speak multiple languages and will be freely available for anyone to use, whether it’s a small startup, a big corporation, or even a government agency.

This is a direct challenge to the current global tech order, where a few giant companies in Silicon Valley often control the latest and greatest AI technology. OpenEuroLLM wants to create a more level playing field, where European countries have the tools and resources to develop their own AI solutions and compete on a global scale.

Leading this charge is a team of experts from top universities and research labs across Europe. They’re combining their expertise in language, technology, and high-performance computing to create AI models that are powerful, reliable, and tailored to the needs of European users.

This is a fascinating development in the AI landscape. It shows that the future of AI is not just about competition between big tech companies but also about collaboration and a shared vision for how this technology can be used to benefit society. It will be interesting to see how OpenEuroLLM evolves and what impact it has on the global AI ecosystem.

They’re joined by an array of European tech luminaries. Among them are Aleph Alpha, the leading light of Germany’s AI sector; Finland’s CSC, which hosts one of the world’s most powerful supercomputers., and France’s Lights On, which recently became Europe’s first publicly-traded GenAI company.

Their alliance has been backed by the European Commission. According to Sarlin, the initiative could be the Commission’s largest-ever AI project. 

“What’s unique about this initiative is that we’re bringing together many Europe’s leading AI organisations in one focused effort, rather than having many small, fragmented projects,” he told TNW via email.

“This concentrated approach is what Europe needs to build open European AI models that eventually enable innovation at scale.”

This European AI alliance isn’t just a scientific endeavor; it’s a strategic move with significant financial backing. They’ve secured a budget of €52 million, plus they have access to some serious computing power, which is like giving them a giant toolbox filled with the latest and greatest AI-building equipment.

This funding comes from a combination of sources, including the European Commission and a special EU program designed to boost investment in key technologies. It shows that Europe is serious about investing in its own AI capabilities and reducing its reliance on technology from other countries.

You see, with the US and China making huge strides in AI, Europe is feeling a bit of pressure. They’re worried about falling behind and losing their influence in the digital world. OpenEuroLLM is like a response to this challenge, a way for Europe to assert its own vision for the future of AI.

And what is that vision? Well, it’s about more than just building powerful AI models. It’s about creating AI that reflects European values, like democracy, transparency, and openness. They want to make sure that AI is used for good and that it benefits everyone in society, not just a select few.

To achieve this, OpenEuroLLM is committed to making its AI models and all the related tools and resources completely open and accessible. This means that anyone can use them, modify them, and build upon them, fostering a spirit of collaboration and innovation across the continent.

They also want to make sure that their AI models respect Europe’s rich linguistic and cultural diversity. This means creating AI that can understand and communicate in many different languages and that reflects the unique cultural nuances of different European countries.

This is all happening at a time when Europe is feeling a bit vulnerable in the tech world. The rapid advancements in AI from the US and China have raised concerns about European companies and even European culture being overshadowed.

OpenEuroLLM is like a bold statement, saying that Europe is not going to sit on the sidelines in the AI revolution. They’re going to actively participate and shape the future of this technology in a way that aligns with their own values and interests.

Sarlin wants OpenEuroLLM to bring new hope to the continent.

”This isn’t about creating a general purpose chatbot—it’s about building the digital and AI infrastructure that enables European companies to innovate with AI,” he said. 

“Whether it’s a healthcare company developing specialized assistants to medical doctors or a bank creating personalized financial services, they need AI models adapted to the context in which they operate and that they can control and own.

“This project is about giving European businesses tools to build models and solutions in their languages that they own and control.”

Training An LLM To Reason: The Importance Of Data Quality And Processing Control

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Image Source: “Data Security Breach” by Visual Content is licensed under CC BY 2.0. https://www.flickr.com/photos/143601516@N03/29723649810

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Imagine you’re trying to teach a student how to solve tricky brain teasers. You wouldn’t just throw a giant pile of random puzzles at them, would you? Instead, you’d carefully pick out a few really good ones that challenge them in different ways, make them think clearly, and are easy to understand.

That’s kind of what these researchers did with an AI model. They wanted to see if they could make the AI better at solving complex problems, but instead of overwhelming it with tons of data, they took a different approach.

They started with a huge collection of almost 60,000 question-answer pairs, like a massive textbook of brain teasers. But instead of using all of them, they handpicked just 1,000 of the best ones.

These examples were like the “goldilocks” puzzles: not too easy, not too hard, but just right. They covered a wide range of topics, were written clearly, and even included helpful hints and explanations, like a teacher guiding a student through the problem.

The researchers also used a special AI called Gemini 2.0 to help them choose the best examples. This AI is like a super-smart tutor that can analyze problems and figure out the best way to solve them. It helped the researchers find examples that would really push the AI model to think critically and creatively.

This new approach shows that sometimes, less is more when it comes to training AI. By focusing on quality over quantity and by giving the AI some flexibility in how it uses its “brainpower,” we can help it become a much better problem-solver. It’s like giving the student the right tools and guidance to unlock their full potential.

Think of it like setting a budget for a detective to solve a case. You can give them a limited amount of time and resources, or you can give them more freedom to investigate thoroughly. This “budget forcing” is what the researchers did with their AI model.

They found that by giving the AI more time to “think”—like” allowing the detective to follow more leads—it could solve problems more accurately. It’s like saying, “Take your time and really dig into this; don’t rush.” And guess what? This more thoughtful AI actually beat out some of the bigger, more data-hungry models from OpenAI on tough math problems!

But here’s the kicker: it wasn’t just about having more data. It was about having the right data. Remember those carefully chosen 1,000 examples? Turns out, they were the secret sauce.

The researchers tried different combinations, like just focusing on difficulty or just on variety, but nothing worked as well as having all three ingredients: difficulty, variety, and quality. It’s like a recipe—you need the right balance of ingredients to make a delicious cake!

And the most surprising part? Even having a massive dataset with almost 60,000 examples didn’t beat those carefully chosen 1,000! It was like having a whole library of books but only needing a few key pages to crack the case.

This shows that being smart about how you train AI is just as important as having lots of data.

So, this “budget forcing” approach is like giving the AI the freedom to think deeply and strategically while also providing it with the right kind of information to learn from. It’s a powerful combination that can lead to some impressive results.

So, while this new AI model with its fancy “budget forcing” trick is pretty impressive, it’s important to remember that it’s still a bit of a specialist. It’s like a star athlete who excels in a few specific events but might not be an all-around champion.

The researchers are being upfront about this and are encouraging others to build on their work by sharing their code and data. It’s like saying, “Hey, we’ve found something cool, but we need your help to explore its full potential!”

This is in contrast to the trend of many research teams trying to create super-smart AI by simply throwing more and more data at the problem. It’s like thinking that if you give a student a mountain of textbooks, they’ll automatically become a genius. But as DeepSeek, that scrappy Chinese company, has shown, sometimes it’s about being clever and resourceful, not just about brute force.

DeepSeek’s success is a reminder that innovation can come from unexpected places and that sometimes the best ideas are the ones that challenge conventional wisdom.

This “budget forcing” technique might be one of those game-changing ideas that helps us unlock the next level of AI intelligence. It’s an exciting time to be following the AI world, as new discoveries and breakthroughs are happening all the time!

DeepSeek’s Latest Open-Source Model DeepSeek-R1 Achieves Comparable Performance To OpenAI’s o1

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Image Source: “Deepseek” by Thiện Ân is marked with Public Domain Mark 1.0. https://www.flickr.com/photos/92423150@N03/54293160994

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DeepSeek, the AI company that’s been making waves, just dropped another bombshell. They’ve released a new language model called DeepSeek-R1 that’s been trained in a really unique way.

Instead of just feeding it tons of data like most AI models, they used a technique called reinforcement learning, where the model learns by trial and error, kind of like how humans learn through experience.

The result? DeepSeek-R1 is a super smart AI that can reason and solve problems like a champ. It’s so good, in fact, that it matches the performance of OpenAI’s latest model on some really tough challenges, like advanced math and coding problems.

What’s even more impressive is that DeepSeek-R1 was built on top of another model they recently released for free. This means they’ve essentially created a super-powered AI by fine-tuning an already powerful one.

They even used a clever trick called knowledge distillation, where they basically taught the smarts of DeepSeek-R1 to other, smaller AI models.

These smaller models ended up outperforming some of the biggest names in the AI world, like GPT-4, on math and coding tasks. Talk about overachievers!

DeepSeek’s approach is groundbreaking because it shows that AI can learn to reason without needing massive amounts of labeled data. It’s like teaching a kid to ride a bike without giving them explicit instructions. They just figure it out through practice and feedback.

Of course, it wasn’t all smooth sailing. DeepSeek’s initial attempts resulted in a model that was a bit rough around the edges.

It was super smart, but it had trouble expressing itself clearly and sometimes mixed up different languages. To fix this, they gave it a little bit of traditional training with carefully selected examples, kind of like giving the AI some extra tutoring.

The end result is DeepSeek-R1, a powerful and versatile AI that can tackle a wide range of tasks, from writing stories to answering questions to summarizing complex information. It’s also really good at understanding long texts, which is a major challenge for most AI models.

DeepSeek’s latest release is another testament to their ability to innovate and push the boundaries of AI. They’ve shown that it’s possible to create incredibly powerful AI models without breaking the bank, and they’re not afraid to share their knowledge with the world.

This is great news for the AI community and could lead to a new wave of innovation in the field.

Within a few days of its release, the LMArena announced that DeepSeek-R1 was ranked #3 overall in the arena and #1 in coding and math. It was also tied for #1 with o1 in “Hard Prompt with Style Control” category.

Django framework co-creator Simon Willison wrote about his experiments with one of the DeepSeek distilled Llama models on his blog:

Each response starts with a <think>…</think> pseudo-XML tag containing the chain of thought used to help generate the response. [Given the prompt] “a joke about a pelican and a walrus who run a tea room together”…It then thought for 20 paragraphs before outputting the joke!…[T]he joke is awful. But the process of getting there was such an interesting insight into how these new models work.

Andrew Ng’s newsletter The Batch wrote about DeepSeek-R1:

DeepSeek is rapidly emerging as a strong builder of open models. Not only are these models great performers, but their license permits use of their outputs for distillation, potentially pushing forward the state of the art for language models (and multimodal models) of all sizes.

DeepSeek’s Success Story: A Potential Challenge For Highly Valued LLM Startups

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Image Source: “deepseek AI” by ccnull.de Bilddatenbank is licensed under CC BY-NC 2.0. https://www.flickr.com/photos/115225894@N07/54291083993

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Imagine a small, scrappy startup going up against giants like Google and Microsoft in the world of AI. That’s DeepSeek, a Chinese company that just dropped a bombshell by releasing a super powerful AI chatbot for free.

This chatbot, called R1, is not only incredibly smart, but it was also shockingly cheap to make. DeepSeek claims they built it for a fraction of the cost of what companies like OpenAI spend on their models.

This has sent shockwaves through the AI world, with investors who poured billions into these big AI companies suddenly sweating bullets.

You see, these investors were betting on companies like OpenAI having a huge advantage because they had tons of money and resources to build these complex AI models.

But DeepSeek just proved that you don’t need a mountain of cash to compete. They built a model that’s so good, it shot to the top of the Apple app store and even caused a massive drop in the stock price of Nvidia, a company that makes the expensive chips needed for AI.

This has everyone rethinking the AI game. Experts are saying this could seriously impact the value of companies like OpenAI, which was recently valued at a whopping $160 billion.

If DeepSeek can achieve similar results with a much smaller budget, it raises questions about whether these sky-high valuations are justified.

Some investors are even questioning the whole open-source approach, where companies share their AI models freely. They’re worried that this will make it even harder to make money in the AI space.

But DeepSeek’s success also shows that there’s still room for smaller players to make a dent in the AI world. It challenges the assumption that you need billions of dollars to build a competitive AI model.

The big question now is whether DeepSeek can actually turn this technical win into real business success. Can they build the relationships and sales teams needed to compete with the established giants in the enterprise market? Only time will tell, but one thing is for sure: DeepSeek has shaken up the AI landscape and forced everyone to rethink the rules of the game.

This David vs. Goliath story in the AI world has everyone buzzing about the future. DeepSeek’s move is like a rogue wave, shaking up the established order and leaving everyone scrambling to adjust.

For the big players like OpenAI, this is a wake-up call. They can no longer assume that their massive investments and exclusive technology will guarantee their dominance.

They need to innovate faster, find ways to reduce costs, and perhaps even rethink their business models to stay ahead of the curve.

For smaller startups and developers, DeepSeek’s success is a source of inspiration. It shows that with ingenuity and smart execution, it’s possible to challenge the giants and make a real impact in the AI world. This could lead to a surge of innovation as more players enter the field, driving competition and pushing the boundaries of what’s possible with AI.

The open-source community is also likely to benefit from DeepSeek’s contribution. By making its model freely available, DeepSeek is empowering researchers and developers around the world to build upon its work and create new and exciting applications.

This could accelerate the pace of AI development and democratize access to this powerful technology.

Of course, DeepSeek’s journey is far from over. They still face the challenge of building a sustainable business and competing with established players in the enterprise market.

But their bold move has already sent ripples throughout the AI landscape, and the aftershocks will be felt for years to come.

This is an exciting time to be following the developments in AI. The competition is heating up, the innovation is accelerating, and the possibilities seem endless.

DeepSeek’s story is a reminder that in the world of technology, disruption can come from anywhere, and the underdogs can sometimes emerge as the victors.

Google’s Breakthrough LLM Architecture: Separating Memory Functions For Enhanced Efficiency And Cost Reduction

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Image Source: “EC-LLM FCO” by airlines470 is licensed under CC BY-SA 2.0. https://www.flickr.com/photos/16103393@N05/34246358573

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Imagine a vast library filled with countless books, each containing a piece of information. Now, imagine a librarian who can instantly access any book in the library and use its knowledge to answer your questions.

This is the power of large language models (LLMs), which have revolutionized how we interact with computers.

However, even the most advanced LLMs have a limited memory. They can only access a certain amount of information at a time, which can be a major bottleneck when dealing with long texts or complex tasks. This is like asking the librarian to answer your questions after reading only a few pages of each book!

Researchers at Google have recently developed a new neural network architecture called Titans that aims to solve this problem.

Titans enhances the memory of LLMs, allowing them to access and process much larger amounts of information without sacrificing efficiency. It’s like giving the librarian a superpower to instantly absorb the knowledge from every book in the library!

The secret behind Titans lies in its unique combination of short-term and long-term memory. Traditional LLMs rely on a mechanism called “attention” to focus on the most relevant parts of the text.

This is like the librarian quickly scanning a book to find the specific information they need. However, attention has its limits. As the text gets longer, the librarian has to scan more pages, which can be time-consuming and inefficient.

Titans overcomes this limitation by introducing a “neural long-term memory” module. This module acts like a separate storage unit where the librarian can store important information for later use. It’s like taking notes or bookmarking important pages in a book.

When the librarian encounters a similar topic later on, they can quickly access their notes and retrieve the relevant information without having to scan the entire book again.

But how does Titans decide what information is worth storing in its long-term memory? It uses a concept called “surprise.” The more unexpected or novel a piece of information is, the more likely it is to be stored.

It’s like the librarian being more likely to remember a surprising plot twist or an unusual character in a book. This ensures that Titans only stores the most valuable and relevant information, making efficient use of its memory capacity.

Furthermore, Titans has an adaptive forgetting mechanism that allows it to discard outdated or irrelevant information. This is like the librarian periodically cleaning up their notes and removing anything that is no longer useful. This ensures that the long-term memory remains organized and efficient.

The researchers have tested Titans on a variety of tasks, including language modeling and long-sequence language tasks. The results are impressive. Titans outperforms traditional LLMs and other memory-enhanced models on many benchmarks, demonstrating its ability to handle long and complex texts.

The development of Titans is a significant step forward in the field of natural language processing. It has the potential to unlock new possibilities for LLMs, enabling them to tackle more challenging tasks and interact with humans in more natural and engaging ways.

Imagine a future where you can have a conversation with an AI assistant that remembers your past interactions and uses that knowledge to provide more personalized and relevant responses. This is the promise of Titans.

The researchers believe that Titans is just the beginning. They plan to continue exploring new ways to enhance the memory and reasoning capabilities of LLMs, paving the way for even more intelligent and human-like AI systems.

As the field of AI continues to evolve, we can expect to see even more groundbreaking innovations that will transform how we live, work, and interact with the world around us.

The implications of Titans’ impressive performance, particularly with long sequences, are significant for enterprise applications. Think of it like upgrading from a small, local library to a massive online archive with instant access to a wealth of information. This is what Titans enables for large language models.

Google, being a leader in the development of long-context models, is likely to integrate this technology into its own models, such as Gemini and Gemma. This means that businesses and developers using these models will be able to leverage the power of Titans to build more sophisticated and capable applications.

One of the key benefits of longer context windows is the ability to incorporate new knowledge directly into the model’s prompt, rather than relying on complex retrieval methods like RAG.

Imagine being able to give an LLM a detailed briefing on a specific topic or task, all within a single prompt. This simplifies the development process and allows for faster iteration and experimentation.

The release of PyTorch and JAX code for Titans will further accelerate its adoption in the enterprise world. Developers will be able to experiment with the architecture, fine-tune it for specific tasks, and integrate it into their own applications.

In essence, Titans represents a significant step towards making LLMs more accessible, versatile, and cost-effective for businesses of all sizes.

By extending the memory and context window of these models, Titans unlocks new possibilities for innovation and automation, paving the way for a future where AI plays an even greater role in our daily lives.

Skip The Hold Music: Google’s AI Will Call Businesses For You

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Image Source: “Old Ericsson Phone” by Alexandre Dulaunoy is licensed under CC BY-SA 2.0. https://www.flickr.com/photos/31797858@N00/2044441912

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Ever wished you had a personal assistant to make those tedious phone calls for you? You know, the ones where you have to navigate endless phone trees, wait on hold, and repeat your request multiple times? Well, Google might be making that wish a reality with its latest experiment: “Ask for Me.”

Imagine this: you’re scrolling through Google Search, looking for a nail salon for a much-needed mani-pedi. You find a place that looks promising, but you’re not sure about their pricing or availability.

Instead of dialing their number and playing phone tag, you see a new button that says “Ask for Me.” Intrigued, you click it.

Suddenly, Google becomes your personal assistant. It asks you a few simple questions: What kind of services are you interested in? Gel polish? Acrylics or a classic French manicure or when are you hoping to book your appointment. Morning, afternoon, or evening? Google takes all your preferences into account and then, get this, it actually calls the salon for you!

Behind the scenes, Google is using its AI-powered calling technology, similar to the Duplex system that can book restaurant reservations and hair appointments.

But Ask for Me goes a step further. It acts as your representative, gathering the information you need without you having to lift a finger.

This feature is currently being tested with nail salons and auto shops. So, if you’re looking for a quick oil change or need to get your tires rotated, Google can handle the initial inquiry for you. Just tell Google what kind of car you have and when you’d like to bring it in, and they’ll take care of the rest.

Of course, Google isn’t just randomly calling businesses on your behalf. Before making the call, you’ll be asked to provide your email address or phone number.

This way the salon or auto shop can get back to you with the information you requested. And you’ll also receive updates from Google about the status of your request.

Now, you might be thinking, “Won’t businesses be freaked out by a robot calling them?” That’s a valid concern, and Google has taken steps to address it.

First of all, every call starts with a clear announcement that it’s an automated system calling from Google on behalf of a user. No hiding behind a synthetic voice pretending to be human!

Secondly, businesses have the option to opt out of these automated calls. They can do this through their Google Business Profile settings or by simply asking Google not to call them during one of these automated calls.

Google wants this to be a helpful tool for both users and businesses, not a source of annoyance.

To further prevent businesses from being bombarded with calls, Google has set call quotas. This means they’ll limit how often a business receives these automated calls.

They’re also being mindful of the data collected during these calls, ensuring that any information gathered is used responsibly and ethically. In fact, Google plans to use the information to improve the system and help other users with similar requests.

Of course, there might still be some initial confusion when a mechanic or nail technician picks up the phone and hears an AI voice on the other end. But as this technology becomes more commonplace, hopefully, those initial surprises will fade away.

Ask for Me is still in its early stages, but it has the potential to revolutionize how we interact with businesses. It could save us time and hassle while also helping businesses manage their inquiries more efficiently.

It’s like having a personal assistant who’s always on call, ready to handle those phone calls we all dread. And as AI technology continues to evolve, who knows what other tasks we’ll be able to delegate to our helpful digital assistants?

Snappier Gemini: Google’s AI App Gets Speed Boost With Flash 2.0

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Image Source: “Orion gets a boost” by NASA Orion Spacecraft is licensed under CC BY-NC-ND 2.0. https://www.flickr.com/photos/71175941@N05/15154991673

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Google just supercharged its Gemini app with a major AI upgrade! Think of it like swapping out your old car engine for a brand new, high-performance model.

Everything runs faster, smoother, and with more power. This isn’t just a minor tweak; it’s a significant leap forward in Gemini’s capabilities.

The star of the show is Gemini 2.0 Flash, the new AI model replacing the older versions. What does this mean for you? Well, get ready for a much more responsive and capable AI companion.

Whether you’re brainstorming ideas for your next project, diving deep into a new subject, or crafting compelling content, Gemini 2.0 Flash is designed to be your ultimate thinking partner.

Imagine you’re writing an article and hit a creative roadblock. Instead of staring blankly at the screen, you can ask Gemini for suggestions, alternative phrasing, or even to generate different outlines to explore new angles.

Need to summarize a complex research paper? Gemini can condense the key findings into easily digestible points. Stuck on a tricky problem? Gemini can help you break it down and explore potential solutions.

This upgrade isn’t limited to a select few; it’s rolling out to all Gemini users, both on the web and mobile apps.

So whether you’re at your desk or on the go, you can tap into the power of Gemini 2.0 Flash. And if you’re feeling a bit nostalgic for the older versions, Gemini 1.5 Flash and 1.5 Pro will still be available for the next few weeks, giving you time to adjust to the new and improved Gemini.

This update isn’t coming out of the blue. Google first announced Gemini 2.0 back in December, generating a lot of buzz in the AI community.

They promised it was “working quickly” to bring this next-generation AI to its products, and they’ve delivered on that promise.

In fact, they even gave some Gemini users a sneak peek with an experimental version of Gemini Flash 2.0 earlier this year.

But that’s not all! Gemini’s image generation capabilities are also getting a significant boost.

Remember those times you wished you could just describe an image and have it appear on your screen? Gemini is getting even better at that, thanks to the newest version of Google’s Imagen 3 AI text-to-image generator.

Imagen 3 is like a digital artist that can translate your words into stunning visuals.

Want a picture of a cat riding a unicorn on a rainbow? Imagen 3 can make it happen. But this new version goes even further, adding richer details and textures to the images it creates. It’s also better at understanding your instructions and generating images that accurately reflect your vision.

This means you can use Gemini to create visuals for presentations, social media posts, or even just for fun.

Imagine being able to generate images for a story you’re writing or create a visual representation of a complex concept you’re trying to understand. The possibilities are endless!

With these upgrades, Google is pushing the boundaries of what’s possible with AI. Gemini is evolving from a simple chatbot into a powerful tool that can augment our creativity, enhance our productivity, and help us explore new ideas.

It’s an exciting time to be exploring the world of AI, and with Gemini 2.0 Flash and Imagen 3, Google is putting cutting-edge AI right at our fingertips.

Say Goodbye To Single Commands: Gemini AI Can Now Perform Multi-Step Actions

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Image Source: “P365x52-12: Control, Option, Command” by kurafire is licensed under CC BY 2.0. https://www.flickr.com/photos/62449696@N00/8375271656

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Google just announced some cool new stuff for its Gemini AI at Samsung’s big phone launch event! Gemini is getting a major upgrade, especially for the latest Samsung phones (like the new S25). But don’t worry, some of these new features will also work on older Samsung S24 and Pixel 9 phones.

The biggest news is that Gemini can now do multiple things in a row. Imagine this: you ask Gemini to find restaurants near you using Google Maps, and then tell it to send a text to your friends inviting them to lunch, all without lifting a finger!

This new “chaining” ability will work on any device with Gemini, but it depends on whether developers have made the apps work with Gemini. Luckily, all the main Google apps already work, and even some Samsung apps like Calendar, Reminders, and Notes are ready to go!

Google’s Gemini is getting even more human-like! Gemini Live, the part that lets you have a conversation with the AI like you would with a friend, is getting a big upgrade, especially when it comes to understanding different kinds of information.

Now, you can show Gemini Live pictures, files, and even YouTube videos! Imagine asking Gemini, “Hey, can you check out this picture of my school project and give me some feedback?” and then actually showing it the picture. That’s what’s possible now.

Unfortunately, this fancy new upgrade only works on the latest Samsung phones (the S24 and S25) and the Pixel 9 for now.

And there’s one more thing! Google is bringing something called “Project Astra” to phones in the next few months, starting with the Galaxy S25 and Pixel phones. This is a whole new kind of AI assistant that lets you interact with the world around you using your phone’s camera.

Picture this: you’re walking down the street and see a cool building. Just point your phone at it and ask Gemini, “What’s the history of this building?” or “What kind of architecture is this?” You can even ask things like, “When is the next bus coming?”

But it gets even cooler. Project Astra is designed to work with Google’s special AI glasses. Imagine wearing these glasses and just asking Gemini questions about what you see, without even having to take out your phone! It’s like having your own personal AI tour guide wherever you go.

Okay, imagine this: you’re walking down the street, and you see this awesome old building. You’re curious about it, but who are you going to ask? With Project Astra, you just whip out your phone, point it at the building, and ask Google’s AI, “Hey, what’s the story with this place?” It’s like having your own personal tour guide in your pocket!

Or let’s say you’re on vacation in a foreign country, and you’re trying to figure out the bus schedule. No problem! Just point your phone at the bus stop, and ask, “When’s the next bus coming?” It’s like magic!

But wait, it gets even cooler. Google is working on these special AI glasses. With these glasses, you can just look at something and ask a question, like, “Hey, what kind of tree is that?” or “How much does that coffee cost?” No phone needed! It’s like having a superpower.

Project Astra is still being developed, but it gives you a taste of what the future could be like. Imagine a world where you can get instant information about anything you see, just by asking. It’s like having the entire internet in your eyeballs!

It’s still early days, but Project Astra has the potential to change the way we learn, explore, and interact with the world around us. It’s a glimpse into a future where technology seamlessly blends with our everyday lives, making everything easier and more fun.