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SpaceX CEO Elon Musk hasn't been shy about his plans to colonize Mars. AP

Elon Musk hasn't been shy about his plans to colonize Mars.

For years, the billionaire has argued that humans must become a multiplanet species as quickly as possible to escape threats on Earth such as overpopulation.

Musk has said that by 2050, he plans to put 1 million people on the neighboring planet, with help from his space-exploration company, SpaceX.

Some of his contemporaries aren't so sure about the plan.

DeepMind CEO Demis Hassabis was among the latest to point out an issue in the plans, The New York Times reported.

While Hassabis agreed the plan could work in theory, Musk was left speechless by Hassabis' suggestion that superintelligent artificial intelligence could follow him to Mars and destroy humanity, the Times reported.

The billionaire hadn't considered that risk and was so concerned he later invested in DeepMind to stay close to the technology, the report said.

Musk, a vocal AI doomsayer, has since launched his own AI company and an AI-powered chatbot.

Hassabis is far from the first to poke holes in the Tesla CEO and X owner's ambitions.

The Microsoft cofounder Bill Gates previously said Musk's ambition to colonize Mars wasn't a good use of money. Gates said funding important healthcare such as vaccine development was a better use of funds.

Four scientists previously told Business Insider that the plan suffered from technical, scientific, and ethical flaws. They said forming a colony on another planetary body, such as the moon, was more realistic than settling on the red planet.

The filmmaker Werner Herzog also took aim at the plans, once saying he thought the proposal was a "mistake" and an "obscenity."Herzog said he thought humans should focus on keeping Earth habitable, rather than looking for a new home.

Hassabis and Musk did not immediately respond to requests for comment made outside normal working hours.

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DeepMind boss found a flaw in Musk's Mars plan. He's not the first. - Business Insider

Google unveiled its highly anticipated new AI model.

Google on Wednesday unveiled its highly anticipated new artificial intelligence model Gemini, an impressive piece of software that can solve math problems, understand images and audio and mimic human reasoning. But Gemini also reveals Googles unique advantage compared to other AI players: Google trained it on its own chips designed in house, not the highly-coveted GPUs the rest of the industry is scrambling to stockpile.

As the AI arms race has heated up, GPUs, or graphics processing units, have become a powerful currency in Silicon Valley. The scrum has turned Nvidia, a company founded 30 years ago that was primarily known for gaming, into a trillion dollar behemoth. The White House has clamped down on chip exports to China, in an attempt to keep the AI prowess of a foreign adversary at bay.

But analysts say the fact that Google DeepMind, the tech giants AI lab, trained its marquee AI model on custom silicon highlights a major advantage large companies have against upstarts, in an age where giants like Google and Microsoft are already under intense scrutiny for their market dominance.

Googles compute hardware is so effective it was able to produce the industrys most cutting edge model, apparently one-upping OpenAIs ChatGPT, which was largely built using Nvidia GPUs. Google claims that Gemini outperforms OpenAIs latest model GPT-4 in several key areas, including language understanding and the ability to generate code. Google said its TPUs allow Gemini to run significantly faster than earlier, less-capable models.

If Google is delivering a GPT-4 beating model trained and run on custom silicon, we believe this could be a sign that AI tech stacks vertically integrated from silicon to software are indeed the future, Fred Havemeyer, head of U.S. AI research at the financial services firm Macquarie, wrote in a note to clients. Havemeyer added, however, that Google is uniquely positioned to make use of custom chips like few others can, flexing its scale, budget, and expertise.

Google showed that it's at least possible, Havemeyer told Forbes. We think that's really interesting because right now the market has been really constrained by access to GPUs.

Big tech companies have been developing their own silicon for years, hoping to wean themselves off of dependency from the chip giants. Google has spent nearly a decade developing its own AI chips, called Tensor Processing Units, or TPUs. Aside from helping to train Gemini, the company has used them to help read the names of the signs captured by its roving Street View cameras and develop protein-folding health tech for drug discovery. Amazon has also launched its own AI accelerator chips, called Trainium and Inferentia, and Facebook parent Meta announced its own chip, MTIA, earlier this year. Microsoft is reportedly working on custom silicon as well, reportedly code-named Athena. Apple, which has long designed its own silicon, unveiled a new chip earlier this year called R1, which powers the companys Vision Pro headset.

Lisa Su, CEO of the chip giant AMD, which has a smaller share of the GPU market, has shrugged off concerns that big tech customers could someday be competitors. Its natural, she told Forbes earlier this year. She said it makes sense for companies to want to build their own components as they look for efficiencies in their operations, but she was doubtful big tech companies could match AMDs expertise built up over decades. I think its unlikely that any of our customers are going to replicate that entire ecosystem.

Googles new model has the potential to shake up the AI landscape. The company is releasing three versions of Gemini with varying levels of sophistication. The most powerful version, a model that can analyze text and images called Gemini Ultra, will be released early next year. The smallest version, Gemini Nano, will be used to power features on Googles flagship Pixel 8 Pro smartphone. The mid-level version, Gemini Pro, is now being used to power Bard, the companys generative chatbot launched earlier this year. The bot initially garnered a lukewarm reception, generating an incorrect answer during a promo video and wiping out $100 billion in Google parent Alphabets market value. Gemini could be Googles best shot at overtaking OpenAI, after a bout of instability last month as CEO Sam Altman was ousted and reinstated in a matter of days.

Google also used the Gemini announcement to unveil the newest version of its custom chips, the TPU v5p, which Google will make available to outside developers and companies to train their own AI. This next generation TPU will accelerate Geminis development and help developers and enterprise customers train large-scale generative AI models faster, allowing new products and capabilities to reach customers sooner, Google CEO Sundar Pichai and DeepMind cofounder Demis Hassabis said in a blog post.

Gemini is the outcome of a massive push inside Google to speed up its shipping of AI products. Last November, the company was caught flat-footed when OpenAI released ChatGPT, a surprise hit that captured the publics imagination. The frenzy triggered a code red inside Google and prompted cofounder Sergey Brin, long absent after leaving his day-to-day role at the company in 2019, to begin coding again. In April, the company merged its two research labs, Google Brain and DeepMind, which had previously been notoriously distinct, in an attempt to give product development a push.

These are the first models of the Gemini era and the first realization of the vision we had when we formed Google DeepMind earlier this year, Pichai said. This new era of models represents one of the biggest science and engineering efforts weve undertaken as a company.

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Google Training Gemini On Its Own Chips Reveals Another Of Its Advantages - Forbes

Teaching algorithms to mimic humans typically requires hundreds or thousands of examples. But a new AI from Google DeepMind can pick up new skills from human demonstrators on the fly.

One of humanitys greatest tricks is our ability to acquire knowledge rapidly and efficiently from each other. This kind of social learning, often referred to as cultural transmission, is what allows us to show a colleague how to use a new tool or teach our children nursery rhymes.

Its no surprise that researchers have tried to replicate the process in machines. Imitation learning, in which AI watches a human complete a task and then tries to mimic their behavior, has long been a popular approach for training robots. But even todays most advanced deep learning algorithms typically need to see many examples before they can successfully copy their trainers.

When humans learn through imitation, they can often pick up new tasks after just a handful of demonstrations. Now, Google DeepMind researchers have taken a step toward rapid social learning in AI with agents that learn to navigate a virtual world from humans in real time.

Our agents succeed at real-time imitation of a human in novel contexts without using any pre-collected human data, the researchers write in a paper in Nature Communications. We identify a surprisingly simple set of ingredients sufficient for generating cultural transmission.

The researchers trained their agents in a specially designed simulator called GoalCycle3D. The simulator uses an algorithm to generate an almost endless number of different environments based on rules about how the simulation should operate and what aspects of it should vary.

In each environment, small blob-like AI agents must navigate uneven terrain and various obstacles to pass through a series of colored spheres in a specific order. The bumpiness of the terrain, the density of obstacles, and the configuration of the spheres varies between environments.

The agents are trained to navigate using reinforcement learning. They earn a reward for passing through the spheres in the correct order and use this signal to improve their performance over many trials. But in addition, the environments also feature an expert agentwhich is either hard-coded or controlled by a humanthat already knows the correct route through the course.

Over many training runs, the AI agents learn not only the fundamentals of how the environments operate, but also that the quickest way to solve each problem is to imitate the expert. To ensure the agents were learning to imitate rather than just memorizing the courses, the team trained them on one set of environments and then tested them on another. Crucially, after training, the team showed that their agents could imitate an expert and continue to follow the route even without the expert.

This required a few tweaks to standard reinforcement learning approaches.

The researchers made the algorithm focus on the expert by having it predict the location of the other agent. They also gave it a memory module. During training, the expert would drop in and out of environments, forcing the agent to memorize its actions for when it was no longer present. The AI also trained on a broad set of environments, which ensured it saw a wide range of possible tasks.

It might be difficult to translate the approach to more practical domains though. A key limitation is that when the researchers tested if the AI could learn from human demonstrations, the expert agent was controlled by one person during all training runs. That makes it hard to know whether the agents could learn from a variety of people.

More pressingly, the ability to randomly alter the training environment would be difficult to recreate in the real world. And the underlying task was simple, requiring no fine motor control and occurring in highly controlled virtual environments.

Still, social learning progress in AI is welcome. If were to live in a world with intelligent machines, finding efficient and intuitive ways to share our experience and expertise with them will be crucial.

Image Credit: Juliana e Mariana Amorim / Unsplash

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This DeepMind AI Rapidly Learns New Skills Just by Watching Humans - Singularity Hub

Google parent Alphabet on December 6 unveiled Gemini, its largest and most capable AI model to date, as the tech giant looks to take on rivals OpenAI's GPT-4 and Meta's Llama 2 in a race to lead the nascent artificial intelligence (AI) space.

This is the first AI model from Alphabet after the merger of its AI research units, DeepMind and Google Brain, into a single division called Google DeepMind, led by DeepMind CEO Demis Hassabis.

Gemini has been built from the ground up and is "multimodal" in nature, meaning it can understand and work with different types of information, including text, code, audio, image and video, at the same time.

The AI model will be available in three different sizes: Ultra (for highly complex tasks), Pro (for scaling across a wide range of tasks) and Nano (on-device tasks).

"These are the first models of the Gemini era and the first realisation of the vision we had when we formed Google DeepMind earlier this year. This new era of models represents one of the biggest science and engineering efforts weve undertaken as a company," said Alphabet CEO Sundar Pichai.

Gemini Pro will be accessible to developers through the Gemini API in Google AI Studio and Google Cloud Vertex AI starting December 13.

On the other hand, Gemini Nano will be accessible to Android developers through AICore, a new system capability introduced in Android 14. This capability will be made available on Pixel 8 Pro devices starting December 6, with plans to extend support to other Android devices in the future.

Google's AI model Gemini will be available in three different sizes: Ultra, Pro and Nano

Gemini Ultra is currently being made available to select customers, developers, partners and safety and responsibility experts for early experimentation and feedback with a broader rollout to developers and enterprise customers early next year.

Also read:Google parent to make 'meaningful' investments to double down on its AI efforts, says CEO Sundar Pichai

Google will also be using Gemini across all its products. Starting December 6, Bard will use a fine-tuned version of Gemini Pro for more advanced reasoning, planning, and understanding.

Meanwhile, Gemini Nano will be powering new features on Pixel 8 Pro smartphones like 'Summarise' in the Recorder app and will soon be available in Smart Reply in Gboard, starting with WhatsApp - with more messaging apps coming next year.

Gemini is also being used to make Google's generative AI search offering Search Generative Experience (SGE) faster for users. The company said that they witnessed a 40 percent reduction in latency in English in the United States, alongside improvements in quality.

Hassabis said that Gemini will be integrated into more of the company's products and services, including Search, Ads, Chrome, and Duet AI in the coming months.

'Transition to AI far bigger than mobile or web'

Pichai said that every technology shift is an opportunity to advance scientific discovery, accelerate human progress and improve lives.

"I believe the transition we are seeing right now with AI will be the most profound in our lifetimes, far bigger than the shift to mobile or the web before it," he said.

Pichai added "AI has the potential to create opportunities - from the everyday to the extraordinary for people everywhere. It will bring new waves of innovation and economic progress and drive knowledge, learning, creativity, and productivity on a scale we havent seen before...Were only beginning to scratch the surface of whats possible."

Alphabet first previewed Gemini in its annual developer conference Google I/O in May 2023. This launch comes at a time when the tech giant is racing to catch up with Microsoft-backed OpenAI which released its latest AI model GPT-4 Turbo during its OpenAI DevDay last month. GPT-4 Turbo is an improved version of the AI upstart's flagship GPT-4 model that was released in March 2023.

Also read:Generative AI helping us reimagine Search, other products: Alphabet's Sundar Pichai

Most flexible model yet

In a blog post, Hassabis said that Gemini Ultra's performance exceeds the current state-of-the-art results on 30 of the 32 widely-used academic benchmarks used in large language model (LLM) research and development.

It is also the first model to outperform human experts on MMLU (massive multitask language understanding) benchmark, which uses a combination of 57 subjects such as math, physics, history, law, medicine and ethics for testing both world knowledge and problem-solving abilities.

Meanwhile, Gemini Pro outperformed GPT-3.5 in six of eight benchmarks including in MMLU and GSM8K (Grade School Math 8K), which measures grade school math reasoning, before its public launch, said Sissie Hsiao, Vice-President, Google Assistant and Bard.

"This is a significant milestone in the development of AI, and the start of a new era for us at Google as we continue to rapidly innovate and responsibly advance the capabilities of our models," Hassabis said.

Hassabis said that for a long time, they wanted to build a new generation of AI models, inspired by the way people understand and interact with the world. "AI that feels less like a smart piece of software and more like something useful and intuitive an expert helper or assistant. Today, were a step closer to this vision," he said.

He mentioned that Gemini is their most flexible model yet since it can run efficiently on everything from data centres to mobile devices and its capabilities will significantly enhance the way developers and enterprise customers build and scale with AI.

Hassabis said that the multimodal reasoning capabilities of the first version of Gemini can help make sense of complex written and visual information, due to which it can extract insights from hundreds of thousands of documents through reading, filtering and understanding information.

He said it also better understands nuanced information and can answer questions relating to complicated topics, making it adept at explaining reasoning in complex subjects like math and physics.

The AI model can also understand, explain, and generate high-quality code in many popular programming languages, like Python, Java, C++ and Go.

"Were working hard to further extend its capabilities for future versions, including advances in planning and memory, and increasing the context window for processing even more information to give better responses," Hassabis said.

Considering Gemini's capabilities, Alphabet is also adding new protections building upon its safety policies and AI principles to tackle potential risks.

"Weve conducted novel research into potential risk areas like cyber-offence, persuasion, and autonomy, and have applied Google Researchs best-in-class adversarial testing techniques to help identify critical safety issues in advance of Geminis deployment," Hassabis said.

The company is also working with a diverse group of external experts and partners to stress-test their models across a range of issues, he said.

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Google unveils Gemini, its largest AI model, to take on OpenAI - Moneycontrol