A 200-acre wide, nearly 300-foot deep pit in the Yana highlands of Siberia, known as the'Batagaika Crater,' is expanding faster than expected due to climate change. Sometimes called the'Gateway to Hell,' the Batagaika Crater first formed when melting'permafrost' soil within the Siberian tundra began to release tons of previously frozen methane, a powerful greenhouse gas, into Earth's atmosphere. Now, new research has discovered that the rate of methane and other carbon gases released as the crater deepens has reached between 4000 and 5000 tons per year. The findings, according to the study's lead author, 'demonstrate how quickly permafrost degradation occurs.' He warns the crater is soon likely to leak all the remaining greenhouse gas it has left.

Amazon said on Wednesday it will pour an additional 2.75bn into Anthropic, bringing its total investment in the artificial intelligence startup to 4bn. The technology giant will maintain a minority stake in San Francisco-based Anthropic, a rival of ChatGPT maker OpenAI. "Generative AI is poised to be the most transformational technology of our time, and we believe our strategic collaboration with Anthropic will further improve our customers' experiences, and look forward to what's next," said Swami Sivasubramanian, vice-president of data and AI at Amazon Web Services, or AWS, Amazon's cloud-computing subsidiary. The Seattle-based tech giant made an initial investment of 1.25bn in Anthropic in September, and indicated then it had plans to invest up to 4bn. Get set for the working day – we'll point you to all the business news and analysis you need every morning The two companies are collaborating to develop so-called foundation models, which underpin the generative AI systems that have captured global attention.

The'Godfather of AI' has warned the tech will be smarter than humans in some ways by the end of the decade - and he believes it will ultimately destroy humanity. In a doom-laden interview with 60 Minutes, Geoffrey Hinton, 75, predicted that in five years, the systems will be surpass human intelligence that would lead to the rise of'killer robots,' fake news and a boom in unemployment. Hinton is a former Google executive credited with creating the technology that became the bedrock of systems like ChatGPT and Google Bard. He recently revealed his fears that the technology could go rogue and write its own code, allowing it to modify itself. While the scientist fears many aspects of the technology, he said AI has huge benefits in healthcare, such as designing drugs and recognizing medical issues.

This article is from Big Technology, a newsletter by Alex Kantrowitz. Amazon's absence from this year's generative–A.I. bonanza has been a bit puzzling. The company invented Alexa, intuiting people's interest in speaking with computers, yet when OpenAI released ChatGPT it seemed to cede the territory. But rather than sitting out the game, Amazon is waiting to play on its terms. Instead of building one A.I. product, it wants a piece of all of them.

Keeping up with an industry as fast-moving as AI is a tall order. So until an AI can do it for you, here's a handy roundup of the last week's stories in the world of machine learning, along with notable research and experiments we didn't cover on their own. The biggest news of the last week (we politely withdraw our Anthropic story from consideration) was the announcement of Bedrock, Amazon's service that provides a way to build generative AI apps via pretrained models from startups including AI21 Labs, Anthropic and Stability AI. Currently available in "limited preview," Bedrock also offers access to Titan FMs (foundation models), a family of AI models trained in-house by Amazon. It makes perfect sense that Amazon would want to have a horse in the generative AI race.

AWS has entered the red-hot realm of generative AI with the introduction of a suite of generative AI development tools. The cornerstone of these is Amazon Bedrock, a tool for building generative AI applications using pre-trained foundation models accessible via an API through AI startups like AI21 Labs, Anthropic, and Stability AI, as well as Amazon's own Titan family of foundation models (FMs). Bedrock offers serverless integration with AWS tools and capabilities, enabling customers to find the right model for their needs, customize it with their data, and deploy it without managing costly infrastructure. Amazon states that the infrastructure supporting the Bedrock service will employ a mix of Amazon's proprietary AI chips (AWS Trainium and AWS Inferentia) and GPUs from Nvidia. AWS is positioning Bedrock as a way to democratize FMs, as training these large models can be prohibitively expensive for many companies.

Amazon is joining the generative AI fray. Bedrock is the company's new API for Amazon Web Services (AWS) that lets developers use and customize AI tools that generate text or images. Think of it as a cloud-based and configurable alternative to OpenAI's ChatGPT and DALL-E 2 aimed at businesses and developers. AWS customers can use Bedrock to write, build chatbots, summarize text, classify images and more based on text prompts. It gives its users a choice of Amazon's Titan foundation model (FM) and several startups' models, including Anthropic's Claude (a Google-backed ChatGPT rival from former OpenAI employees), AI21's Jurassic-2 (a language model specializing in Spanish, French, German, Portuguese, Italian and Dutch) and Stable Diffusion (a popular open-source image generator).

The seeds of a machine learning (ML) paradigm shift have existed for decades, but with the ready availability of scalable compute capacity, a massive proliferation of data, and the rapid advancement of ML technologies, customers across industries are transforming their businesses. Just recently, generative AI applications like ChatGPT have captured widespread attention and imagination. We are truly at an exciting inflection point in the widespread adoption of ML, and we believe most customer experiences and applications will be reinvented with generative AI. AI and ML have been a focus for Amazon for over 20 years, and many of the capabilities customers use with Amazon are driven by ML. Our e-commerce recommendations engine is driven by ML; the paths that optimize robotic picking routes in our fulfillment centers are driven by ML; and our supply chain, forecasting, and capacity planning are informed by ML. Prime Air (our drones) and the computer vision technology in Amazon Go (our physical retail experience that lets consumers select items off a shelf and leave the store without having to formally check out) use deep learning.

Artificial intelligence (AI) is "completely living up to its expected hype and hysteria," with 70% of daily digital interactions being AI-based, and businesses generating multimillion-dollar revenue streams from it. This was the word from Mike Bugembe, founder of UK-based AI consultancy, Lens.ai, delivering a keynote at the ITWeb Business Intelligence Summit 2020, in Johannesburg, today. Bugembe is a bestselling author, international speaker and executive advisor, helping organisations use data and AI to transform their businesses and grow. Discussing the importance of an AI strategy to gain business value, Bugembe noted that companies across the globe are ramping up investments in AI-related technologies and gaining multimillion-dollar-revenue streams, cutting costs, managing risk, improving operations, and finding innovative ways to develop products and strengthen customer intimacy. However, he warned that without an intelligent roadmap, companies risk focusing on the wrong opportunities, resulting in failure to tap into the true promise of AI. "Business and technology experts believe AI will be the most significant technological revolution that businesses have ever experienced," said Bugembe.

In a recent episode of the McKinsey & Co. podcast, McKinsey Global Institute partner Michael Chui explains the role that simulation plays in training AI: To more deeply understand why this is, we can think of machine learning and simulation as being two different approaches for understanding and predicting the behavior of complex adaptive systems. Individually they are both powerful modeling paradigms, but it is together, working in concert, that they show the greatest promise. Machine learning automates the building of analytical models. The models are constructed using algorithms that learn from data. These algorithms are able to update models in real-time, giving them the ability to generate real-time predictions.