And they’re faster than AI.
Researchers at Cortical Labs, a biotechnology startup, have successfully taught human brain cells in a petri dish how to play the 2D table tennis simulation video game “Pong”.
The team managed to create mini-brains consisting of 800,000 to one million living human brain cells in a petri dish, reports New Scientist. Regarding the unlikely study, Brett Kagan, chief scientific officer at Cortical Labs and research lead of the project, says We think… See more.
And they could soon help explore the deep sea.
New York-based firm Pliant Energy Systems is building a marine system reminiscent of the cuttlefish with its rippling underwater motion, a report from The Economist reveals.
The company’s biomimetic machine, called Velox, is based on the principle that propellers are nowhere near as efficient as the fins of sea creatures that are prevalent in nature.
Tesla has started to release a new Full Self-Driving Beta software update (10.7) that improves the situation with “phantom braking” and even helps the FSD beta be more energy efficient.
For more than a year now, Tesla has been slowly rolling out what it is calling “Full Self-Driving Beta” (FSD Beta), which is an early version of its self-driving software that is currently being tested by a fleet of Tesla owners selected by the company and through its “safety test score.”
The software enables the vehicle to drive autonomously to a destination entered in the car’s navigation system, but the driver needs to remain vigilant and ready to take control at all times.
Lightelligence, the global optical computing innovator, revealed its Photonic Arithmetic Computing Engine (PACE), the company’s latest platform to fully integrate photonics and electronics in a small form factor.
As Lightelligence’s first demonstration of optical computing for use cases beyond AI and deep learning, PACE efficiently searches for solutions to several of the hardest computational math problems, including the Ising problem, and the graph Max-Cut and Min-Cut problems, illustrating the real-world potential of integrated photonics in advanced computation.
Visit https://www.lightelligence.ai/ to learn more.
The end of the year is a time not just for predictions of top trends but also to watch for the biggest hype and most misleading recommendations that get dished out to business leaders. There’s no scarcity of these in the artificial intelligence (AI) space.
As AI evolves, its influence on humanity continues to rise. People often focus on AI’s ability to automate and amplify tasks but underestimate its more profound impact on society. “Very few human creations have had the kind of impact as AI,” says Loomis. He compares it with the invention of language—a “tool” that has changed the trajectory of humans and helped birth civilizations. Today, we are still taking baby steps with AI. However, unlike early humans, we are waking up to the fact that AI is not just a tool but will weave deeper into our society.
“I hope 2022 will be the start of this realization, where we don’t just create new technical practices for AI but also understand how it shapes us. This should alert us to the fact that this is the time to lay the guardrails—the checks and balances needed to guide this change into something greater and not dystopian,” concludes Loomis.
Advancing Veterinary Care With Predictive Diagnostics, AI & One Health Principles — Dr. Jennifer Ogeer, DVM, Antech Diagnostics, Mars Petcare, Mars Inc.
Dr. Jennifer Ogeer, DVM, MSC, MBA is Vice President of Medical Science & Innovation at Antech Diagnostics (https://www.antechdiagnostics.com/), one of the world’s largest reference laboratory networks, and a unit of Mars Veterinary Health (https://www.marsveterinary.com/).
Dr. Ogeer is also Chair of the Board Of Directors of Veterinarians Without Borders (https://www.vetswithoutborders.ca/), an organization that works with governments, educational institutions, non-governmental organizations, local communities, farmers’ groups, and international agencies, to tackle root-cause issues affecting public health, animal health and ecosystem health in developing communities around the world.
Dr. Ogeer is also Vice-Chair of the Diversify Veterinary Medicine Coalition (https://diversifyvetmed.org/) which is working to bring greater diversity to the veterinary profession.
Dr. Ogeer is a graduate of the Ontario Veterinary College (OVC), University of Guelph, Canada. She completed an emergency medicine/critical care residency at Tufts University/Angell Memorial Animal Hospital in Boston and a Master of Science degree in Critical Care at the Ontario Veterinary College. She also has completed an Executive MBA at Western University (Canada).
Dr. Ogeer is a highly experienced residency-trained emergency and critical care veterinarian with a rich and diverse background in clinical practice, academic teaching/education, research and business management consulting.
As a former associate professor she has worked in specialty referral hospitals and several university teaching hospitals, including University of Guelph, University of Saskatchewan and Texas A&M University.
Dr. Ogeer is an active member of the veterinary community, and has published various articles in peer-reviewed journals and conducted research on hospital-acquired infections and developed protocols for infectious disease outbreak management and prevention.
How close are we to having fully autonomous vehicles on the roads? Are they safe? In Chandler, Arizona a fleet of Waymo vehicles are already in operation. Waymo sponsored this video and provided access to their technology and personnel. Check out their safety report here: https://waymo.com/safety/
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References:
Waymo Safety Reports — https://waymo.com/safety/
Driving Statistics — https://ve42.co/DrivingStats.
The Real Moral Dilemma of Self-Driving Cars https://ve42.co/SelfDriving.
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One of the most important issues in contemporary societies is the impact of automation and intelligent technologies on human work. Concerns with the impact of mechanization on jobs and unemployment go back centuries, at least since the late 1,500 ’ s, when Queen Elizabeth I turned down William Lee ’ s patent applications for an automated knitting machine for stockings because of fears that it might turn human knitters into paupers. [2] In 1936, an automotive industry manager at General Motors named D.L. Harder coined the term “automation” to refer to the automatic operation of machines in a factory setting. Ten years later, when he was a Vice President at Ford Motor company, he established an “Automation Department” which led to widespread usage of the term. [3]
The origins of intelligent automation trace back to US and British advances in fire-control radar for operating anti-aircraft guns to defend against German V-1 rockets and aircraft during World War II. After the war, these advances motivated the MIT mathematician Norbert Weiner to develop the concept of “cybernetics”, a theory of machines and their potential based on feedback loops, self-stabilizing systems, and the ability to autonomously lean and adapt behavior. [4] In parallel, the Dartmouth Summer Research Project on Artificial Intelligence workshop was held in 1956 and is recognized as the founding event of artificial intelligence as a research field. [5]
Since that decade, workplace automation, cybernetic-inspired advanced feedback systems for both analogue and digital machines, and digital computing based artificial intelligence (together with the overall field of computer science) have advanced in parallel and co-mingled with one another. Additionally, opposing views of these developments have co-existed with one side highlighting the positive potential for more capable and intelligent machines to serve, benefit and elevate humanity, and the other side highlighting the negative possibilities and threats including mass unemployment, physical harm and loss of control. There has been a steady stream of studies from the 1950 ’ s to the present assessing the impacts of machine automation on the nature of work, jobs and employment, with each more recent study considering the capability enhancements of the newest generation of automated machines.
Who better to answer the pros and cons of artificial intelligence than an actual AI?
Students at Oxford’s Said Business School hosted an unusual debate about the ethics of facial recognition software, the problems of an AI arms race, and AI stock trading. The debate was unusual because it involved an AI participant, previously fed with a huge range of data such as the entire Wikipedia and plenty of news articles.
Over the last few months, Oxford University Alex Connock and Andrew Stephen have hosted sessions with their students on the ethics of technology with celebrated speakers – including William Gladstone, Denis Healey, and Tariq Ali. But now it was about time to allow an actual AI to contribute, sharing its own views on the issue of … itself.
The AI used was Megatron LLB Transformer, developed by a research team at the computer chip company Nvidia and based on work by Google. It was trained by consuming more content than a human could in a lifetime and was asked to defend and question the following motion: “This house believes that AI will never be ethical.”
Biotechnology is a curious marriage of two seemingly disparate worlds. On one end, we have living organisms—wild, unpredictable celestial creations that can probably never be understood or appreciated enough, while on the other is technology—a cold, artificial entity that exists to bring convenience, structure and mathematical certainty in human lives. The contrast works well in combination, though, with biotechnology being an indispensable part of both healthcare and medicine. In addition to those two, there are several other applications in which biotechnology plays a central role—deep-sea exploration, protein synthesis, food quality regulation and preventing environmental degradation. The increasing involvement of AI in biotechnology is one of the main reasons for its growing scope of applications.
So, how exactly does AI impact biotechnology? For starters, AI fits in neatly with the dichotomous nature of biotechnology. After all, the technology contains a duality of its own—machine-like efficiency combined with the quaintly animalistic unpredictability in the way it works. In general terms, businesses and experts involved in biotechnology use AI to improve the quality of research and for improving compliance with regulatory standards.
More specifically, AI improves data capturing, analysis and pattern recognition in the following biotechnology-based applications:
