Lifeboat Foundation

Deep North, a Foster City, California-based startup applying computer vision to security camera footage, today announced that it raised $16.7 million in a Series A-1 round. Led by Celesta Capital and Yobi Partners, with participation from Conviction Investment Partners, Deep North plans to use the funds to make hires and expand its services “at scale,” according to CEO Rohan Sanil.

Deep North, previously known as Vmaxx, claims its platform can help brick-and-mortar retailers “embrace digital” and protect against COVID-19 by retrofitting security systems to track purchases and ensure compliance with masking rules. But the company’s system, which relies on algorithms with potential flaws, raises concerns about both privacy and bias.

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Oh the things we can see and accomplish when time and death can no longer hinder us.

Immortality is eternal life, being exempt from death, unending existence.
Human beings seem to be obsessed with the idea of immortality. But a study published in the journal Proceedings of the National Academy of Sciences has stated, through a mathematical equation, that it is impossible to stop ageing in multicellular organisms, which include humans, bringing the immortality debate to a possible end.
So you probably don’t want to die, most people don’t. But death takes us all no matter what we want. However, today in our scenario, humans have found a way to obtain that immortality. Watch the whole timeline video to find out how reaching immortality changes the world and the way we live.

Continue reading “Timeline: What If Humans Were Immortal” »

Smart image analysis algorithms, fed by cameras carried by drones and ground vehicles, can help power companies prevent forest fires.

Every time a human or machine learns how to get better at a task, a trail of evidence is left behind. A sequence of physical changes — to cells in a brain or to numerical values in an algorithm — underlie the improved performance. But how the system figures out exactly what changes to make is no small feat. It’s called the credit assignment problem, in which a brain or artificial intelligence system must pinpoint which pieces in its pipeline are responsible for errors and then make the necessary changes. Put more simply: It’s a blame game to find who’s at fault.

AI engineers solved the credit assignment problem for machines with a powerful algorithm called backpropagation, popularized in 1986 with the work of Geoffrey Hinton, David Rumelhart and Ronald Williams. It’s now the workhorse that powers learning in the most successful AI systems, known as deep neural networks, which have hidden layers of artificial “neurons” between their input and output layers. And now, in a paper published in Nature Neuroscience in May, scientists may finally have found an equivalent for living brains that could work in real time.

A team of researchers led by Richard Naud of the University of Ottawa and Blake Richards of McGill University and the Mila AI Institute in Quebec revealed a new model of the brain’s learning algorithm that can mimic the backpropagation process. It appears so realistic that experimental neuroscientists have taken notice and are now interested in studying real neurons to find out whether the brain is actually doing it.

(2021). Nuclear Technology: Vol. 207 No. 8 pp. 1163–1181.

Focusing on nuclear engineering applications, the nation’s leading cybersecurity programs are focused on developing digital solutions to support reactor control for both on-site and remote operation. Many of the advanced reactor technologies currently under development by the nuclear industry, such as small modular reactors, microreactors, etc., require secure architectures for instrumentation, control, modeling, and simulation in order to meet their goals. ¹ Thus, there is a strong need to develop communication solutions to enable secure function of advanced control strategies and to allow for an expanded use of data for operational decision making. This is important not only to avoid malicious attack scenarios focused on inflicting physical damage but also covert attacks designed to introduce minor process manipulation for economic gain. ²

These high-level goals necessitate many important functionalities, e.g., developing measures of trustworthiness of the code and simulation results against unauthorized access; developing measures of scientific confidence in the simulation results by carefully propagating and identifying dominant sources of uncertainties and by early detection of software crashes; and developing strategies to minimize the computational resources in terms of memory usage, storage requirements, and CPU time. By introducing these functionalities, the computers are subservient to the programmers. The existing predictive modeling philosophy has generally been reliant on the ability of the programmer to detect intrusion via specific instructions to tell the computer how to detect intrusion, keep log files to track code changes, limit access via perimeter defenses to ensure no unauthorized access, etc.

Continue reading “Covert Cognizance: A Novel Predictive Modeling Paradigm” »

In 2,021 Instagram will be the most popular social media platform. Recent statistics show that the platform now boasts over 1 billion monthly active users. With this many eyes on their content, influencers can reap great rewards through sponsored posts if they have a large enough following with this many eyes on their content. The question for today then becomes: How do we effectively grow our Instagram account in the age of algorithmic bias? Instagram expert and AI growth specialist Faisal Shafique help us answer this question utilizing his experience growing his @fact account to about 8M followers while also helping major, edgy brands like Fashion Nova to over 20M.

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Duke professor becomes second recipient of AAAI Squirrel AI Award for pioneering socially responsible AI.

Whether preventing explosions on electrical grids, spotting patterns among past crimes, or optimizing resources in the care of critically ill patients, Duke University computer scientist Cynthia Rudin wants artificial intelligence (AI) to show its work. Especially when it’s making decisions that deeply affect people’s lives.

Continue reading “A ‘New Nobel’ — Computer Scientist Wins $1 Million Artificial Intelligence Prize” »

GPT-3 was meant to understand and construct natural language. But as these tools prove, it’s pretty good at programming languages, too.

At the outbreak of World War I, the French army was mobilized in the fashion of Napoleonic times. On horseback and equipped with swords, the cuirassiers wore bright tricolor uniforms topped with feathers—the same get-up as when they swept through Europe a hundred years earlier. The remainder of 1914 would humble tradition-minded militarists. Vast fields were filled with trenches, barbed wire, poison gas and machine gun fire—plunging the ill-equipped soldiers into a violent hellscape of industrial-scale slaughter.

Capitalism excels at revolutionizing war. Only three decades after the first World War I bayonet charge across no man’s land, the US was able to incinerate entire cities with a single (nuclear) bomb blast. And since the destruction of Hiroshima and Nagasaki in 1,945 our rulers’ methods of war have been made yet more deadly and “efficient”.

Continue reading “Algorithms of war: The military plan for artificial intelligence” »