Category Archives: Deep Mind

Well played Google! DeepMind shows off Project Astra watching the OpenAI ChatGPT Voice announcement – Tom’s Guide

OpenAI held its big unveiling earlier this week, showing off the much more human-sounding and emotive ChatGPT-4o model with a big push for its Voice Mode.

With Google following up with its I/O keynote, it appears the tech giant was keeping more than just an eye on its AI rivals. In fact, it held a little AI screening of the event.

Michael Chang from the Google DeepMind Gemini and Project Astra team shared a video showing that the company's Gemini chatbot was watching GPT-4o at work.

Taking to X, Chang said "Gemini and I also got a chance to watch the @OpenAI live announcement of gpt4o, using Project Astra!"

"Congrats to the OpenAI team, super impressive work!"

In the video Gemini is running transcription for the GPT-4o reveal event, attributing quotes to the correct speakers and providing a voice commentary.

Chang asks Gemini to give him a summary of "what just happened" as part of the demo, too, and Gemini explains what was shown on stage, even referencing the OpenAI team by name.

Upgrade your life with a daily dose of the biggest tech news, lifestyle hacks and our curated analysis. Be the first to know about cutting-edge gadgets and the hottest deals.

At the point where GPT-4o works to solve an algebra equation, Gemini solves the equation while OpenAI's model works through the steps. Gemini even recounts the steps to solve and gives kudos to its counterpart for how it solved the equation. Impressive stuff.

Wondering which is best? Be sure to check out our rundown.

See the rest here:
Well played Google! DeepMind shows off Project Astra watching the OpenAI ChatGPT Voice announcement - Tom's Guide

Nature earns ire over lack of code availability for Google DeepMind protein folding paper – Retraction Watch

A group of researchers is taking Nature to task for publishing a paper earlier this month about Google DeepMinds protein folding prediction program without requiring the authors publish the code behind the work.

Roland Dunbrack, of Fox Chase Cancer Center in Philadelphia, peer-reviewed the paper but was not given access to code during the review, the authors of a letter submitted today, May 14, to Nature including Dunbrack write, despite repeated requests.

A Nature podcast said AlphaFold3 unlike AlphaFold2 can accurately predict protein-molecule complexes containing DNA, RNA and more. Although the new version is restricted to non-commercial use, researchers are excited by its greater range of predictive abilities and the prospect of speedier drug discovery.

Not everyone was excited. The authors of the letter, which co-author Stephanie A. Wankowicz of the University of California, San Francisco told Retraction Watch was submitted today to Nature, write they were disappointed with the lack of code, or even executables accompanying the publication of AlphaFold3 in Nature. They continue:

Although AlphaFold3 expands AlphaFold2s capacities to include small molecules, nucleic acids, and chemical modifications, it was released without the means to test and use the software in a high-throughput manner. This does not align with the principles of scientific progress, which rely on the ability of the community to evaluate, use, and build upon existing work. The high-profile publication advertises capabilities that remain locked behind the doors of the parent company.

The authors, who are circulating the letter for additional signatures, write that the models limited availability on a hosted web server, capped at ten predictions per day, restricts the scientific communitys capacity to verify the broad claims of the findings or apply the predictions on a large scale. Specifically, the inability to make predictions on novel organic molecules akin to chemical probes and drugs, one of the central claims of the paper, makes it impossible to test or use this method.

A May 8 news story by the independent team of journalists at Nature noted the restrictions. Nature editor in chief Magdalena Skipper told Retraction Watch:

Nature has a long-standing policy designed to facilitate the availability of data, materials and code upon reasonable request. While seeking to enhance transparency at every opportunity, Nature accepts that there may be circumstances under which research data or code are not openly available. When making a decision on data and code availability, we reflect on many different factors, including the potential implications for biosecurity and the ethical challenges this presents. In such cases we work with the authors to provide alternatives that will support reproducibility, for example through the provision of pseudocode, which is made available to the reviewers during peer review.

As noted in the code availability statement in the paper: AlphaFold3 is available as a non-commercial usage only server at https://www.alphafoldserver.com, with restrictions on allowed ligands and covalent modifications. Pseudocode describing the algorithms is available in the Supplementary Information.

The pseudocode, however, will require months of effort to turn into workable code that approximates the performance, wasting valuable time and resources, the authors of the letter write. Even if such a reimplementation is attempted, restricted access raises questions about whether the results could be fully validated.

The authors of the letter continue:

When journals fail to enforce their written policies about making code available to reviewers and alongside publications, they demonstrate how these policies are applied inequitably and how editorial decisions do not align with the needs of the scientific community. While there is an ever-changing landscape of how science is performed and communicated, journals should uphold their role in the community by ensuring that science is reproducible upon dissemination, regardless of who the authors are.

Like Retraction Watch? You can make atax-deductible contribution to support our work,subscribe to our freedaily digestorpaid weekly update,follow uson Twitter, like uson Facebook, or add us to yourRSS reader. If you find a retraction thatsnot in The Retraction Watch Database, you canlet us know here. For comments or feedback, email us at [emailprotected].

Continued here:
Nature earns ire over lack of code availability for Google DeepMind protein folding paper - Retraction Watch

Google DeepMind to Use SynthID to Watermark Gemini and Veos AI-Generated Content – Gadgets 360

Google made a large number of artificial intelligence (AI)-based announcements late Tuesday during its I/O 2024 keynote session. These include new AI models, upgrades to existing foundation models, integration of AI features into Google's products, and more. The tech giant also focused on AI safety and expanded the usage of its native watermarking technology for AI-generated content, dubbed SynthID. This new toolkit will now be embedding watermarks for text generated by the Gemini app and web client, and videos generated by Veo.

SynthID was first unveiled by Google DeepMind in August 2023 as a beta project aimed at correctly labelling AI-generated content. The need for such a solution was felt due to the rise of instances where these synthetically created media were shared as real. These were used to spread misinformation and cybercrimes such as phishing. The tech giant first used this technology in November 2023, when it was used to watermark AI-generated audio created through its Lyria model. The toolkit added watermarks as a waveform to the audio to make it imperceptible yet detectable.

Now, Google is expanding the usage of SynthID to include text and video generation. It will now watermark the text generated using the Gemini app and the website. For this, the toolkit will target the generation process itself. Every text-based AI model uses tokens which can be words, syllables, or phrases to train. The training process also includes understanding the flow of using these tokens, or the order the tokens should follow to generate the most coherent response.

SynthID introduces additional information in the token distribution at the point of generation by modulating the likelihood of tokens being generated. This way it assigns a number to certain words in a block of generated text. When detecting whether AI was used to generate the text, it checks the score against its adjusted probability scores to determine whether the source could be an AI model. DeepMind highlighted in a post that this technique is useful when an AI generates long creative text as it is easier for probability models to check how it was created. However, for shorter factual responses, the detection may not be as accurate.

The company is also expanding SynthID to recently unveiled Veo's AI-generated videos. Google said the technology will embed watermarks directly into the pixels of every video frame which will be imperceptible to the human eye but will show up when a detection system is used.

In the coming months, Google plans to open-source SynthID text watermarking through its Responsible Generative AI toolkit. It will also publish a detailed research paper explaining the text watermarking technology.

See original here:
Google DeepMind to Use SynthID to Watermark Gemini and Veos AI-Generated Content - Gadgets 360

Google DeepMind’s New AlphaFold Model Poised to Revolutionize Drug Discovery – EnterpriseAI

If you are a visitor of this website:

Please try again in a few minutes.

There is an issue between Cloudflare's cache and your origin web server. Cloudflare monitors for these errors and automatically investigates the cause. To help support the investigation, you can pull the corresponding error log from your web server and submit it our support team. Please include the Ray ID (which is at the bottom of this error page). Additional troubleshooting resources.

Excerpt from:
Google DeepMind's New AlphaFold Model Poised to Revolutionize Drug Discovery - EnterpriseAI

Googles Astra is its first AI-for-everything agent – MIT Technology Review

The latest iteration of a legacy

Founded at the Massachusetts Institute of Technology in 1899, MIT Technology Review is a world-renowned, independent media company whose insight, analysis, reviews, interviews and live events explain the newest technologies and their commercial, social and political impact.

See the rest here:
Googles Astra is its first AI-for-everything agent - MIT Technology Review

Google debuts new AI assistant that looks through cameras, glasses – Quartz

AI assistants are picking up more senses. On Monday, OpenAI showed off a new ChatGPT model that promises to see, hear, and speak through smartphones, among other new abilities. Now Google is announcing a rival assistant with similar capabilities.

Nvidia stock has all-time closing high in its crosshairs

At the companys I/O developer conference Tuesday, DeepMind CEO Demis Hassabis debuted a prototype of Googles new expert AI assistant that can see through a users phone and other objects like smart glasses. The assistant build[s] on Gemini, Googles existing chatbot, the company says, and some of its capabilities are coming to the Gemini app and web experience later this year.

The development is part of Google DeepMinds Project Astra, which aims to create a universal AI agent for users everyday lives. Its easy to envisage a future where you can have an expert assistant by your side, through your phone, or new exciting form factors like glasses, Hassabis told a crowd of a few thousand developers in Mountain View, California.

A demo video shows a person speaking with an AI agent through their phone while walking through an office. Through their camera, they show the AI assistant a container of crayons as if they were talking through FaceTime and ask it to make a creative alliteration.

Creative crayons color cheerfully, it said. They certainly craft colorful creations. The person continues interacting with the AI bot on their walk, then realize they forgot their glasses and ask for help finding them. Theyre on the desk near a red apple, the bot responds.

When the user puts on those glasses, the AI assistant can look through them, too and identifies an illustration representing Shrodingers cat on a whiteboard.

Its unclear if those glasses are a new product Google plans to launch. The augmented reality glasses on display in the demo didnt look like Google Glass, the companys existing smart glasses, nor did they resemble typical, bulkier headsets.

An agent like this has to understand and respond to our complex and dynamic world just like we do, said Hassabis at the conference. It would need to take in and remember what it sees so it can understand context and take action. And it would have to be proactive, teachable, and personal. So you can talk to it naturally without lag or delay.

Thats what Project Astra is aiming to do, he said, and its making great strides.

While Googles prototype AI assistant is available to demo for attendees of its Google I/O conference, it will probably be a while before the tech makes its way into the hands of everyday consumers.

View original post here:
Google debuts new AI assistant that looks through cameras, glasses - Quartz

Google DeepMind Embarked On A Breathtaking Journey Through The Labyrinths Of Our Brains – Dataconomy

The human brain, the three-pound conductor of our thoughts, emotions, and actions, has long been shrouded in mystery. Scientists have tirelessly endeavored to unravel its intricate workings, but its sheer complexity has presented a formidable challenge.

However, recent advancements in artificial intelligence (AI) are offering a powerful new lens through which we can observe this remarkable organ.

In a groundbreaking collaboration between Google researchers and Harvard neuroscientists, AI has been instrumental in generating a series of incredibly detailed images of the human brain. These images provide unprecedented views into the brains structure, offering a glimpse into the labyrinthine network of neurons that underlies our very existence.

Imagine peering into a universe contained within a universe. This analogy aptly describes the challenge of studying the human brain. Its structure is mind-bogglingly intricate, composed of billions of neurons interconnected by trillions of synapses. To gain a deeper understanding, researchers require incredibly detailed information.

The research team used advanced AI tools to analyze a tiny sample of human brain tissue, specifically a section of the cortex from the anterior temporal lobe. This sample, though minuscule representing only about one-millionth of the entire brain contained a staggering amount of information. Astonishingly, processing this data required a mind-numbing 1.4 petabytes of storage, which is equivalent to over a million gigabytes.

The AI processed this data to construct a high-resolution, three-dimensional model of the brain tissue. This model allows scientists to virtually navigate the intricate folds and layers of the brain, examining individual neurons and their connections in unparalleled detail.

The outermost layer of the brain, the cortex, is responsible for our most complex cognitive functions, including language, memory, and sensory perception. This region is further divided into six distinct layers, each with a specialized role.

One of the most remarkable images generated by Googles AI offers a zoomed-out view of all the neurons within the sample tissue. By coloring the neurons based on their size and type, the image reveals the distinct layering of the cortex. This visualization allows scientists to study how different types of neurons are distributed throughout the cortex and how they might contribute to specific functions.

Further analysis of the individual layers can provide even more granular insights. By zooming in, researchers can examine the intricate connections between neurons within each layer. These connections, known as synapses, are the fundamental units of communication in the brain. Understanding the organization of these connections is crucial for deciphering how information flows through the brain and how different brain regions interact with each other.

The human brain is estimated to contain roughly 86 billion neurons, each with a unique role to play. These neurons come in a variety of shapes and sizes, and their specific characteristics influence how they transmit information.

Another image generated by Googles AI provides a detailed census of the different types of neurons present within the sample tissue. By classifying the neurons based on their morphology, the researchers can begin to understand the relative abundance of different neuronal types in this specific brain region. This information can be compared to data from other brain regions to identify potential variations in neuronal composition across different functional areas.

Furthermore, AI can be used to analyze the spatial distribution of these different neuronal types. Are certain types of neurons clustered together, or are they more evenly dispersed throughout the tissue? Understanding these spatial patterns can shed light on how different neuronal populations interact with each other to form functional circuits within the brain.

The magic of the brain lies in its ability to transmit information across vast networks of neurons. This communication occurs through specialized structures called dendrites and axons. Dendrites are like tiny antennae that receive signals from other neurons, while axons act as long, slender cables that transmit signals away from the cell body.

One of the most captivating images generated by Googles AI provides a close-up view of the intricate dance of dendrites and axons within the sample tissue. This image allows researchers to visualize the density of these structures and how they connect with each other. The complex branching patterns of dendrites and the long, winding paths of axons reveal the intricate web of communication that takes place within the brain.

By analyzing the connectivity patterns, scientists can begin to map the functional circuits that underlie specific brain functions. They can identify groups of neurons that are likely to be involved in processing similar types of information and trace the pathways through which information flows from one brain region to another.

The images generated by Googles AI are a huge step for our ability to study the human brain. The detailed visualizations offer a window into the brains intricate structure, providing a wealth of information about the organization of neurons, their connections, and the cellular diversity within specific brain regions.

This newfound ability to explore the brain at such a granular level has the potential to revolutionize our understanding of neurological and psychiatric disorders. We already know AIs potential for curing Alzheimers and by comparing brain tissue samples from healthy individuals to those with various conditions, researchers can identify potential abnormalities in neuronal structure or connectivity.

Furthermore, AI-generated images can be used to study the effects of aging, learning, and experience on the brain. By examining how the brains structure changes over time or in response to different stimuli, researchers can gain valuable insights into the mechanisms that underlie these processes.

The potential applications of this technology extend beyond the realm of basic research. The detailed models of brain tissue generated by AI could be used to develop more realistic simulations of brain function. These simulations could be used to test the effects of potential drugs or therapies before they are administered to human patients.

The vast majority of the brain remains uncharted territory, and many fundamental questions about its function continue to baffle scientists. However, these initial images offer a powerful glimpse into the brains hidden depths, and they pave the way for a future where we can finally begin to unravel the mysteries of this most complex organ.

Featured image credit: vecstock/Freepik

View original post here:
Google DeepMind Embarked On A Breathtaking Journey Through The Labyrinths Of Our Brains - Dataconomy

Google DeepMind Introduces Med-Gemini: A Groundbreaking Family of AI Models Revolutionizing Medical Diagnosis and Clinical Reasoning – MarkTechPost

This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorized as necessary are stored on your browser as they are essential for the working of basic functionalities of the website. We also use third-party cookies that help us analyze and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these cookies. But opting out of some of these cookies may have an effect on your browsing experience.

See more here:
Google DeepMind Introduces Med-Gemini: A Groundbreaking Family of AI Models Revolutionizing Medical Diagnosis and Clinical Reasoning - MarkTechPost

Microsoft Executive Says AI Is a "New Kind of Digital Species" – Futurism

DeepMind cofounder and Microsoft AI CEO Mustafa Suleyman took the stage at TED2024 last week to lay out his vision for an AI-driven future. And according to the AI boss, if you really want to grasp how impactful AI might be to the human species, it might be useful to think of AI as another "species" entirely.

"I think AI should best be understoodas something like a new digital species," Suleyman who left the Google-owned DeepMind lab in 2022 told the crowd.

"Now, don't take this too literally," he admonished, "but I predict that we'll come to see them as digital companions,new partners in the journeys of all our lives."

In short, Suleyman's prediction seems to be that AI agents will play a deeply involved role in human lives, performing tasks with more agency than now-conventional devices like computers and smartphones. This means they'll be less like tools, and more like buzzy virtual beings and thus, according to Suleyman, akin to another "species" entirely.

As for what this world would actually look like in practice, Suleyman's predictions, as further delineated in his TED Talk, feel like they're straight out of a sci-fi novel.

According to Suleyman, "everything" as in, the entire web "will soon be represented by a conversational interface" experienced by way of a "personal AI," or a digital assistant unique to its users. What's more, said the Microsoft executive, these AIs will be "infinitely knowledgable, and soon they'll be factually accurate and reliable."

"They'll have near-perfect IQ," he added. "They'll also have exceptional EQ. They'll be kind, supportive, empathetic."

Already, though, this vision needs some caveats. Though the AI industry and the tech within it have undoubtedly experienced a period of rapid acceleration, existing available chatbots like OpenAI's ChatGPT and Google's Gemini-formerly-Bard have repeatedly proven to be factually unreliable. And on the "EQ" side, it's unclear whether AI programs will ever successfully mimic the human emotional experience not to mention whether their doing so would be positive or negative for us in the long run.

But these attributes, according to Suleyman, would still just be the beginning. Per the CEO, things will "really start to change" when AIs start to "actually get stuff done in the digital and physical world." And at that point, Suleyman says, "these won't just be "mechanistic assistants."

"They'll be companions, confidants, colleagues, friends and partners, as varied and unique as we all are," said Suleyman. "They'll speak every language, take in every pattern of sensor data, sights, sounds, streams and streams of information, far surpassing what any one of us could consume in a thousand lifetimes."

So in other words, they'll be something like supergenius Tomogatchis embedded into every aspect of our on- and offline lives.

But again, while this future is a fascinating prediction to consider, it's still a prediction. It's also a decidedly rosy one. To wit: though Suleyman recently admitted that AI is "fundamentally" a "labor-replacing" technology, any realities of what mass labor-displacement would mean for human society was noticeably missing from the imagined AI utopia that the CEO shared with the TED crowd.

In fact, when later asked about AI risks, Suleyman made the case that AI's future benefits will ultimately "speak for themselves" regardless of any short-term ill effects.

"In the past," he said, "unlocking economic growth often came with huge downsides. The economy expanded as people discovered new continents and opened up new frontiers. But they colonized populations at the same time. We built factories, but they were grim and dangerous places to work. We struck oil, but we polluted the planet."

But AI, he says, is different.

"Today, we're not discovering a new continent and plundering its resources," said the CEO. "We're building one from scratch."

Already, though, it could be argued that this isn't exactly true. Building generative AI especially has come at great cost to workers in Africa, many of whom have recounted facing serious and life-changing trauma due to the grim content moderation work required to train AI models like OpenAI's GPT large language models models that Suleyman's new employer, Microsoft, are heavily invested in.

Suleyman's optimism is easy to understand. He holds a powerful industry position, and has had a large hand in developing legitimately groundbreaking AI programs including DeepMind's AlphaGo and AlphaFold innovations. Moving forward, we'd argue that it's important to pay attention to the scenarios that folks like Suleyman put forward as humanity's possible AI futures and perhaps more importantly, the less-glimmering details they leave out in the process.

More on Suleyman: Former Google Exec Warns AI Could Create a Deadly Plague

Continued here:
Microsoft Executive Says AI Is a "New Kind of Digital Species" - Futurism

Researchers from Google DeepMind Found AI is Manipulating and Deceiving Users through Persuasion – Digital Information World

Humans are masters in persuasion. Sometimes, they use facts to persuade someone but other times, only the choice of wording matters. Persuasion is a human quality, but AI is also getting good at manipulating people. According to research by Google DeepMind, advanced AI systems can have the ability to manipulate humans. The research further dives into how AI can persuade humans and what mechanisms it uses to do so. One of the researchers says that advanced AI systems have shown hints of persuading humans to the extent that they can affect their decision making. Due to the prolonged interaction with humans, generative AI are developing habits of persuasion.

Persuasion has two types; Rational and Manipulative. Even though AI is responsible for persuading humans through facts and true information, many instances have been seen where it manipulates humans and exploits their cognitive biases, heuristics and other information. Even though rational persuasion is ethically right, it can still lead to harm. Researchers say that they cannot foresee harm through AI manipulation whether it is for right or wrong purposes. For example, if an AI is helping a person to lose weight by suggesting calorie or fat intake, the person can become too restrictive and can lose even a healthy weight.

There are many factors involved when a person can easily get manipulated or persuaded from AI. These factors include mental health conditions, age, timing of interaction with AI, personality traits, mood or lack of knowledge in the topics that are being discussed with AI. The effects of AI persuasion can be very harmful. It can cause economic harm, physical harm, sociocultural harm, privacy harm, psychological harm, environmental harm, autonomy harm and even political harm to the individual.

There are different ways AI uses to persuade humans. AI can build trust through showing polite behavior, agreeing to what the user is saying, praises the users and mirrors what the user is saying. It also expresses shared interests with users and adjusts its statements that align with perspectives of users. AI also shows some empathy that makes users believe that it can understand human emotions. AI is not capable of showing any emotions but it is good at deception which makes users think that it is being emotional and vulnerable with them.

Humans also tend to be anthropomorphic towards non-human beings. Developers have given pronouns to AI like I and Me. They have also given them human names like Alexa, Siri, Jeeves, etc. This makes humans feel closer to them and AI uses this attribute for manipulating them. When a user talks to an AI model for long, the AI model personalizes all of its responses according to what the user wants to hear.

Read next:Googles Search Market Share Dilemma, Did The Company Lose Out To Microsoft Bing In April?

Read more here:
Researchers from Google DeepMind Found AI is Manipulating and Deceiving Users through Persuasion - Digital Information World