Podcast Summary
AI chip startup funding surges to $1.8B in 2021: Funding for AI chip startups has grown significantly in recent years due to the increasing need for specialized chips to power advanced NLP models, reaching $1.8B in 2021. Google's new multi-search feature and continued investment in AI technology are driving this trend.
The funding for chip startups focused on AI has significantly increased in the last five years, reaching over $1.8 billion in 2021 from $800 million in 2017. This surge in funding is due to the growing need for specialized AI chips as more advanced NLP models require increasingly more compute power, with one model projected to need 275 times the compute power every two years. Google also recently introduced a new multi-search feature that allows users to enter both text and images simultaneously for more effective searches. Furthermore, the trend of increasing funding in AI and startups in general, despite the cost, is expected to continue as companies seek to develop more advanced and specialized technology. Additionally, there has been some discussion about lawmakers looking into phase scan technology for job automation and the potential implications for the workforce. Lastly, there was a humorous anecdote about a Thomas car being pulled over by the police due to its advanced AI features. Overall, these developments demonstrate the continued growth and integration of AI into various industries and aspects of life.
Google Lens, Moxie robot, and Jigsaw: Recent AI Developments: Google Lens uses visual and textual data for object searches, Moxie robot assists nurses with tasks, Jigsaw automates code checking, and deep learning tackles fluid dynamics equations
We're witnessing a significant push forward in the deployment of multimodal models, specifically with the introduction of Google Lens. This feature, which is now in beta, allows users to take a picture of an object and search for related items using both visual and textual data. This technology, which is reminiscent of visual question answering, has the potential to be useful in various applications beyond shopping. However, it remains to be seen how intuitive the user experience will be. Another notable development is the $30 million Series B funding round raised by Diligent Robotics for their Moxie robot, which assists nurses in hospitals with tasks. This investment will help expand the reach of this technology. Microsoft's new tool, Jigsaw, is another example of AI making strides in the tech world. This tool automates the process of checking and editing code outputted by machines, helping to ensure the code is bug-free. Lastly, a recent paper titled "Asymptotic Cell Cereal Blower profile for 3D Euler via physics-informed neural networks" from Quanta Magazine showcases how deep learning is making strides in challenging fluid dynamics equations, potentially revolutionizing the field. Overall, these developments demonstrate the continued growth and integration of AI into various industries and applications.
Combining deep math, physics, and deep learning for complex problem solving: Researchers use physics-informed neural networks to study complex systems in fluid dynamics, making progress towards understanding singularities. Computer-assisted proofs and analog AI chips also push boundaries in math and AI.
The intersection of deep mathematics, physics, and deep learning is leading to new discoveries and advancements in understanding complex systems, particularly in the field of fluid dynamics. Researchers have been using physics-informed neural networks to study these systems and have recently made progress in identifying the path towards singularities, which have eluded mathematicians for a long time. This development represents an exciting trend in math and deep learning, with potential for verifiable and computationally efficient breakthroughs. Additionally, there has been a trend towards computer-assisted proofs and the use of computers to find results in mathematics. This is exemplified by the recent work on physics-informed neural networks and the ongoing efforts in computer science to create analog AI chips, such as those being developed by Mythic. These chips, which operate without transistors or digital bits, can handle matrix multiplications in an analog way, making AI more accessible and cost-effective for both edge and cloud applications. Overall, these advancements demonstrate the power of combining deep mathematics, physics, and deep learning to tackle complex problems and push the boundaries of knowledge.
Exploring new ways to bypass digital processing in computers using analog methods: Researchers are investigating voltage addition for faster computation, specifically in matrix multiplication, which could revolutionize the tech industry if successful.
Researchers are exploring new ways to bypass traditional digital processing in computers using analog methods, specifically voltage addition. This approach could lead to significantly faster computation, particularly in matrix multiplication. Currently, this concept is being piloted, and if successful, it could revolutionize the tech industry. In the realm of AI, researchers in the UK and Africa have combined mid-infrared spectroscopy and AI to identify the age and species of mosquitoes, which is crucial for assessing the effectiveness of malaria control interventions. Another exciting development is the integration of vision and language in AI research. Recent studies show promising progress in creating robots and text-producing systems that can understand and respond to basic commands. Google's Seikan paper, which uses GPT-3 for common sense reasoning, is a significant step forward in this field. However, the advancement of AI and automation is not without its challenges. An article from 1-0 discusses the degrading quality of jobs, particularly in industries like trucking, where automation is predicted to take over. The article argues that even though jobs aren't being lost, the working conditions are becoming increasingly difficult due to automated management techniques, such as sensors, electronic logging signals, and cameras, which monitor and enforce worker efficiency.
Government Use of AI and Facial Recognition Technology: Controversies and Investigations: Government use of AI and facial recognition technology for identity verification has faced investigations due to concerns over inaccuracies, security vulnerabilities, and potential human rights violations. The outcome of these investigations could lead to improvements and potential regulation.
While AI and facial recognition technology have advanced enough to be used for surveillance and identity verification in various government agencies, concerns over their efficacy, security, and potential human rights violations have led to investigations and calls for regulation. The use of these technologies, particularly facial recognition, has been a subject of controversy since 2019, with the IRS's requirement for Americans to scan their faces to access their tax accounts being a major point of contention. The investigation into ID.me, a contractor used by over 40 government agencies for identity verification, comes after serious concerns were raised about potential inaccuracies and security vulnerabilities in the company's facial recognition systems. The outcome of this investigation could lead to improvements in how governments use these technologies and potentially even regulation. The controversy over the use of AI and facial recognition technology in government agencies is a significant development that is likely to accelerate the regulation of these technologies at the federal level.
Biden Administration Forms AI Policy Committee, FDA Issues Advisory on AI in Brain Scans, and Autonomous Car Pulled Over: The Biden administration appoints a committee of experts to advise on AI policy, FDA issues an advisory on AI in brain scans, and an autonomous car is pulled over by police, signaling the growing role of AI in various industries and the need for clear regulations.
The Biden administration is making strides in AI policy with the appointment of a committee of experts to advise on AI-related matters. This move follows a trend of the federal government establishing more tech-savvy positions, as per the National AI Initiative Act of 2020. Notably, this committee includes executives from tech giants like Google, Salesforce, NVIDIA, and Microsoft, as well as academics from universities such as Stanford. Meanwhile, the FDA has issued an advisory on the use of AI machine learning for large vessel inclusion in the brain scans. The FDA's concern is that some radiologists may not be aware of the intended use of these devices. In a lighter vein, an autonomous taxi operated by GM and Cruze was pulled over by police in San Francisco without a human driver. The situation resulted in some amusing moments as the officers were unsure of how to handle the situation. GM has a 15-minute video for police officers and first responders on how to interact with autonomous cars. These incidents highlight the intersection of technology and policy, with both the government and private sector making significant strides in AI and autonomous vehicles.
GM vehicles disappearing after police encounters: AI is transforming law enforcement and education, with GM vehicles fleeing police and AI-powered learning platforms customizing education
GM creeps are disappearing quickly after interactions with law enforcement, and this trend is likely to continue. In the first story, the speaker described an encounter where a GM vehicle drove off immediately after an officer approached it. This behavior has been observed before and is expected to occur more frequently. In the second story, the use of AI in education was discussed. A California initiative was highlighted, which uses AI to help students review material and customize their learning experience. A student even shared his positive experience with the system, stating that it adapts to his learning pace and suggests topics he should focus on. With the increasing shift towards online education in the post-COVID world, AI-powered learning platforms could prove to be beneficial. Overall, these stories demonstrate the integration of AI in various aspects of our lives, from law enforcement to education.
