Champ Suthipongchai is a General Partner at Creative Ventures, a method-driven venture capital firm based in the San Francisco Bay Area. Among these, only 5% are decacorns, commanding a valuation of $10 billion or more. Unbeknownst to many, one-third of the decacorns are deep tech companies, commanding more than $500 billion in aggregate valuation. They are not always the household names we hear, but they are already among us. Deep tech is nothing new.
After months of testing, Citizen, the crime and neighborhood watch app, is releasing Protect, a subscription-based feature that lets users contact virtual agents for help if they feel they're in danger. According to Citizen, the feature can connect users with a Protect agent either through video, audio, or text available around the clock. The company said audio and text-only communication allows users to discreetly call for help "in difficult situations" where they might not be able to or are scared to be seen calling 911. Protect began beta testing earlier this year as the feature has been available to 100,000 users, Citizen said. The new feature comes as Citizen currently has more than 8 million users who have sent out more than billion alerts in major U.S. cities including New York, Los Angeles, Chicago, Atlanta, Houston and the San Francisco Bay Area.
Hamburg News: How did you enter the AI sector? I started programming at my first employer, Axel Springer, out of curiosity. At the time, I was allowed to jointly found a start-up for Axel Springer and spent plenty of time working with our programmers, who encouraged and motivated me to programme. Later, I gained my first real programming experience at Soundcloud where I heard about artificial intelligence for the first time. I jumped onto the AI bandwagon through my Master's degree in America and later as part of my job in San Francisco. Hamburg News: What do you plan to do in future?
When a loved one passes, will we continue to communicate with the deceased through artificial intelligence? While that sounds like an episode of Black Mirror, the beginnings of a digital afterlife with some potentially positive ramifications recently took place with one man, as Jason Fagone reports in the San Francisco Chronicle. His story centers around writer Joshua Barbeau, a 33-year old who had lost his fiancee eight years earlier from a rare liver disease. At home one night, he accessed a site called Project December. As Fagone notes, the site is "powered by one of the world's most capable artificial intelligence systems, a piece of software known as GPT-3. It knows how to manipulate human language, generating fluent English text in response to a prompt."
SAN FRANCISCO, CA - SEPTEMBER 07: Google AI Research Scientist Timnit Gebru speaks onstage during ... [ ] Day 3 of TechCrunch Disrupt SF 2018 at Moscone Center on September 7, 2018 in San Francisco, California. 'Taking On Tech is an informative series that explores artificial intelligence, data science, algorithms, and mass censorship. In this inaugural report, For(bes) The Culture kicks things off with Dr. Timnit Gebru, a former researcher and co-lead of Google's Ethical AI team. When Gebru was forced out of Google after refusing to retract a research paper that was already cleared by Google's internal review process, a conversation about the tech industry's inherent diversity problem resurfaced. The paper raised concerns on algorithmic bias in machine learning and the latent perils that AI presents for marginalized communities. Around 1,500 Google employees signed a letter in protest, calling for accountability and answers over her unethical firing.
All the sessions from Transform 2021 are available on-demand now. MoEngage, an AI customer engagement platform based in San Francisco, California, today announced that it raised $32.5 million led by Multiples Private Equity with participation from Eight RoadsVentures, F-Prime Capital, and Matrix Partners. The company says that the new capital, which brings MoEngage's total raised to over $72 million, will be used to support its global growth strategy and strengthen its AI and predictive capabilities. Generally speaking, a positive customer experience translates to high customer spend. Eighty-six percent of buyers are willing to pay more for a great customer experience, according to one source.
Earlier this month, two groups unveiled the culmination of years of work by computer scientists, biologists, and physicists: advanced modeling programs that can predict the precise 3D atomic structures of proteins. Last week, the biggest payoff of that work arrived. One team used its newly minted artificial intelligence (AI) programs to solve the structures of 350,000 proteins from humans and 20 model organisms, such as Escherichia coli bacteria, yeast, and fruit flies, all mainstays of biological research. In the coming months, the group says it plans to expand its efforts to all cataloged proteins—some 100 million molecules. “It's pretty overwhelming,” says John Moult, a protein folding expert at the University of Maryland, Shady Grove, who runs a biennial competition called the Critical Assessment of protein Structure Prediction (CASP). Moult says structural biologists have dreamed for decades that accurate computer models would one day augment slow, painstaking experimental methods, such as x-ray crystallography, that map protein shapes with extreme precision. “I never thought the dream would come true,” Moult says. The computer model, called AlphaFold, is the work of researchers at DeepMind, a U.K. AI company owned by Alphabet, the parent company of Google. In fall of 2020, AlphaFold swept the CASP competition, tallying a median accuracy score of 92.4 out of 100 for its predicted structures, well ahead of the next closest competitor ( Science , 4 December 2020, p. ). But because DeepMind researchers didn't reveal AlphaFold's underlying computer code, other teams were left frustrated, unable to build on the progress. That began to change this month ( Science , 16 July, p. ). On 15 July, researchers led by Minkyung Baek and David Baker at the University of Washington, Seattle, reported online in Science that they had created a competing system: a highly accurate protein structure prediction program called RoseTTAFold, which they released publicly. The same day, Nature rushed out details of AlphaFold in a paper by DeepMind researchers led by Demis Hassabis and John Jumper. Both programs use AI to spot folding patterns in vast databases of solved protein structures. The programs compute the most likely structure of unknown proteins by applying those patterns and also considering basic physical and biological rules governing how neighboring amino acids in a protein interact. In their paper, Baek and Baker used RoseTTAFold to create a structure database of hundreds of G-protein coupled receptors, a class of common drug targets. Now, DeepMind researchers report in Nature that they have amassed 350,000 predicted structures—more than twice as many as experimenters have solved in many decades of work. AlphaFold's structures for which the researchers say they have high confidence cover nearly 44% of all human proteins. AlphaFold determined that many of the remaining human proteins were “disordered,” meaning their shape doesn't adopt a single structure. Such disordered proteins may ultimately adopt a structure when they bind to a protein partner, Baker says. They may also naturally adopt multiple conformations, says David Agard, a structural biologist at the University of California, San Francisco. A database of DeepMind's new protein predictions, assembled with collaborators at the European Molecular Biology Laboratory (EMBL), is freely accessible online. “It's fantastic they have made this available,” Baker says. “It will really increase the pace of research.” Because the 3D structure of a protein largely dictates its function, the DeepMind library is apt to help biologists sort out how thousands of unknown proteins do their jobs. “We at EMBL believe this will be transformative to understanding how life works,” says the lab's director general, Edith Heard. “This will be one of the most important data sets since the mapping of the human genome,” adds Ewan Birney, director of EMBL's European Bioinformatics Institute. DeepMind collaborators say that by making it possible to quickly assess how a change in a protein's sequence alters its structure and function, AlphaFold has already spurred the development of novel enzymes for breaking down plastic waste. It has also prompted efforts to better target parasitic diseases. The impacts aren't likely to stop there. The predictions will help experimentalists who solve structures, Baek says. Data from x-ray crystallography and cryo–electron microscopy experiments can be difficult to interpret, Baek and others say, and having a model can help pinpoint the correct structure. “In the short term, it will boost structure determination efforts,” she predicts. “And over time it will also slowly replace [experimental] structural determination efforts.” If that happens, structural biologists won't find themselves out of work. Baker notes that both experimental and computational scientists are already beginning to turn their efforts to the more complex challenge of understanding exactly which proteins interact with one another and what molecular changes happen during these interactions. The new tools will “reset the field,” Baker says. “It's a very exciting time.” : http://www.sciencemag.org/content/370/6521/1144 : http://www.sciencemag.org/content/373/6552/262
SAN FRANCISCO--July 28, 2021-- The American College of Radiology Data Science Institute (ACR DSI) and the American Academy of Ophthalmology today announced a collaboration that will expand ACR DSI's groundbreaking AI-LAB platform to include eye care. Leveraging use cases and data from the Academy, this collaboration will accelerate the use of machine learning in the ophthalmic industry to the benefit of patients across the globe. "We've now made it easier for the ophthalmology community to access real world examples for our own use cases. By working together with ACR, we are leveraging a platform developed for the radiology community to educate our own community about AI development and encouraging new AI to be developed that will benefit our specialty," said Tamara R. Fountain, MD, president of the American Academy of Ophthalmology. The Academy will provide the ophthalmology content and the ACR will provide the IT infrastructure to integrate the use cases and datasets into the landmark AI-LAB.
Deep Genomics, an artificial intelligence startup founded by the University of Toronto's Brendan Frey, has secured US$180 million from investors, including Japanese multinational Softbank and Canada Pension Plan Investments, the Globe and Mail reported. Launched in 2015, the startup uses machine learning to develop treatments for genetic diseases. According to the Globe and Mail, Deep Genomics currently has 10 drugs in pre-clinical development, four of which are set to enter human trials by mid-2023. It is also working with San Francisco Bay-area biopharmaceutical company BioMarin Pharmaceutical Inc. to identify drug candidates for rare diseases. "These are all new chemical entities that would not exist" without Deep Genomics' technology," Frey, who is CEO of Deep Genomics and a professor in U of T's Faculty of Applied Science & Engineering, told the Globe.
Graphics processing units from Nvidia are too hard to program, including with Nvidia's own programming tool, CUDA, according to artificial intelligence research firm OpenAI. The San Francisco-based AI startup, which is backed by Microsoft and VC firm Khosla ventures, on Wednesday introduced the 1.0 version a new programming language specially crafted to ease that burden, called Triton, detailed in a blog post, with the link to GitHub source code. OpenAI claims Triton can deliver substantial ease-of-use benefits over coding in CUDA for some neural network tasks at the heart of machine learning forms of AI such as matrix multiplies. "Our goal is for it to become a viable alternative to CUDA for Deep Learning," the leader of the effort, OpenAI scientist Philippe Tillet, told ZDNet via email. Triton "is for machine learning researchers and engineers who are unfamiliar with GPU programming despite having good software engineering skills," said Tillet.