Last update: Monday 12/4/23
... Click the "start" button in the audio control (below) to hear the podcast. If audio fails to start, or gets stuck, try reloading the page.
1) Amazon's "re:Invent 2023" customer conference (Nov 27-Dec 1, Las Vegas, NV)
Amazon is playing catchup with regards to the deployment of generative AI technology; more specifically, Amazon is playing catchup to Microsoft.
Generative Ai's large language models (LLMs) must be run in the cloud because of their massive demand for graphics processing units (GPUs), the kind of chips made by Nvidia. Amazon invented cloud services and still has a much larger share of the global market for cloud services than Microsoft.
- Amazon's AWS share = 33 percent
- Microsoft's Azure share = 23 percent
Note: Percentages from Google search for market share Amazon AWS, Microsoft Azure
How did Microsoft overcome this disadvantage? Microsoft's Office dominates the global office productivity market with a 49 percent share. So Microsoft packaged its generative AI services as productivity enhancing "copilots" for the components of its office productivity suite. Enterprise subscribers to Microsoft Office will purchase additional subscriptions for copilots.
Moreover, at its recent developers conference, Microsoft announced its intention to manufacture its own GPUs, thereby reducing the dependency of its Azure cloud services on Nvidia's limited supply.
Given this context, few were surprised by Amazon's announcements at its "re:Invent" conference of an array of generative AI services that it would provide for enterprise customers of its AWS cloud services. Its two most memorable announcement were for its chatbot and its image generator:
- Amazon's chatbot is called "Q"
- Its image generator is called "Titan Image Generator"
Note that "Titan" is the name of Amazon's LLM. Also note that Amazon had previously announced its intention to produce its own GPU chips called "Trainium". At re:Invent, it announced the development of more powerful "Trainium" chips
2) Google's DeepMind AI program called "GNoME" identified 2.2 million stable crystal structures
The second paragraph from the Ars Technica article listed in the "A. Top Stories" section (below) provides a succinct summary of this achievement:
The second paragraph from the Ars Technica article listed in the "A. Top Stories" section (below) provides a succinct summary of this achievement:
"The trove of theoretically stable but experimentally unrealized combinations identified using an AI tool known as GNoME is more than 45 times larger than the number of such substances unearthed in the history of science, according to a paper published in Nature on Wednesday."
If this were the first major breakthrough in science/technology that had been achieved by Google's DeepMind, one would wonder why so many people seem to think that ChatGPT is the most powerful AI system in the world and therefore the most likely forbear of Artificial General Intelligence (AGI) ... but this isn't DeepMind's first breakthrough nor its greatest. Three other recent major breakthroughs are noted below in section "B. Three other breakthroughs ... ".
To date, neither ChatGPT nor any competitor LLM systems have created significant new knowledge about any science or about any technology. DeepMind's success is not a coincidence. Its founding purpose was to do just that: to create AI tools that would greatly assist in the resolution of the world's biggest problems.
3) "The Inside Story of Microsoft’s Partnership with OpenAI"
This long investigative piece in the New Yorker is a "must read", because it provides a plausible description of Microsoft's side of its partnership with OpenAI.
Microsoft's management of this partnership reflected the views of Kevin Scott, Microsoft's chief technology officer, the person most responsible for forging the OpenAI partnership. The following quotation from the New Yorker captures the essential features of his vision:
Microsoft's management of this partnership reflected the views of Kevin Scott, Microsoft's chief technology officer, the person most responsible for forging the OpenAI partnership. The following quotation from the New Yorker captures the essential features of his vision:
"The discourse around A.I., he believed, had been strangely focussed on science-fiction scenarios—computers destroying humanity—and had largely ignored the technology’s potential to “level the playing field,” as Scott put it, for people who knew what they wanted computers to do but lacked the training to make it happen. He felt that A.I., with its ability to converse with users in plain language, could be a transformative, equalizing force—if it was built with enough caution and introduced with sufficient patience.
Scott and his partners at OpenAI had decided to release A.I. products slowly but consistently, experimenting in public in a way that enlisted vast numbers of nonexperts as both lab rats and scientists: Microsoft would observe how untutored users interacted with the technology, and users would educate themselves about its strengths and limitations. By releasing admittedly imperfect A.I. software and eliciting frank feedback from customers, Microsoft had found a formula for both improving the technology and cultivating a skeptical pragmatism among users. The best way to manage the dangers of A.I., Scott believed, was to be as transparent as possible with as many people as possible, and to let the technology gradually permeate our lives—starting with humdrum uses. And what better way to teach humanity to use A.I. than through something as unsexy as a word processor?"
Whereas DeepMind strives to create AI tools that will enable the world's greatest scientists and technologists to address the world's biggest problems, Microsoft's efforts are grounded in AI's potential to level the playing field by their accessibility to all users in plain language.
A. Top 3 stories in past week ...
- Other LLMs
Amazon's re:Invent 2023 conference (Nov 27-Dec 1, Las Vegas, NV) ***
-- Overview "Here’s everything Amazon Web Services announced at AWS re:Invent", Christine Hall, TechCrunch, 11/29/23
-- Amazon Q, an AI-powered chatbot for AWS customers ..."Amazon Introduces Q, an A.I. Chatbot for Companies", Karen Weise, NY Times, 11/28/23 ... This story also covered by Bloomberg, VentureBeat, Gizmodo,
-- Image generator "Amazon joins AI image creation fray with new model", Emilia David, The Verge, 11/29/23 ... This story also covered by Bloomberg, Engadget, Gizmodo, - Non-LLM News
"Google’s DeepMind finds 2.2M crystal structures in materials science win", Michael Peel, Ars Technica, 11/29/23 ... "The trove of theoretically stable but experimentally unrealized combinations identified using an AI tool known as GNoME is more than 45 times larger than the number of such substances unearthed in the history of science, according to a paper published in Nature on Wednesday."
-- This story also covered by Wired, VentureBeat, Nature, Science, - OpEds
"The Inside Story of Microsoft’s Partnership with OpenAI", Charles Duhigg, The New Yorker, 12/1/23 ***
B. Three other breakthroughs in science/technology using DeepMind's AI/Neural Networks
- "One of the Biggest Problems in Biology Has Finally Been Solved", Tanya Lewis, Scientific American, 10/31/22 ... protein foldimg problem ...DeepMind
-- This story also covered by VentureBeat, CNET, Science, - "Courtesy of AI: Weather forecasts for the hour, the week and the century", Devin Coldewey, TechCrunch, 11/14/23 ... DeepMind
-- This story also covered by Gizmodo, Financial Times, MIT Tech Review, Ars Technica, Wired, ZDNet,
-- These articles describe research published in Nature and Science - "AI Tool Pinpoints Genetic Mutations That Cause Disease", Even Callaway & Nature Magazine, Scientific American, 11/21/23 ... DeepMind
-- This story also covered by Wall Street Journal, Wired,
C. Links to ChatGPT's responses to five basic AI questions:
ChatGPT's answer to each question provides the framework for its response to the next question. So most readers should start with Question 1.
- Question 1: What is Artificial Intelligence?
Question 2: What is Machine Learning?
Question 3: What are Neural Networks?
Question 4: What are GPT models?
Question 5: Have models other than GPT made substantial progress towards AGI?
No comments:
Post a Comment
Your comments will be greatly appreciated ... Or just click the "Like" button above the comments section if you enjoyed this blog note.