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Category: information science

What if computers could recognize objects as well as the human brain could? Electrical engineers at the University of California, San Diego have taken an important step toward that goal by developing a pedestrian detection system that performs in near real-time (2−4 frames per second) and with higher accuracy (close to half the error) compared to existing systems. The technology, which incorporates deep learning models, could be used in “smart” vehicles, robotics and image and video search systems.

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Around the world, cities are choking on smog. But a new AI system plans to analyze just how bad the situation is by aggregating data from smartphone pictures captured far and wide across cities.

The project, called AirTick, has been developed by researchers from Nanyang Technological University in Singapore, reports New Scientist. The reasoning is pretty simple: Deploying air sensors isn’t cheap and takes a long time, so why not make use of the sensors that everyone has in their pocket?

The result is an app which allows people to report smog levels by uploading an image tagged with time and location. Then, a machine learning algorithm chews through the data and compares it against official air-quality measurements where it can. Over time, the team hopes the software will slowly be able to predict air quality from smartphone images alone.

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Individual brain cells within a neural network are highlighted in this image obtained using a fluorescent imaging technique (credit: Sandra Kuhlman/CMU)

Carnegie Mellon University is embarking on a five-year, $12 million research effort to reverse-engineer the brain and “make computers think more like humans,” funded by the U.S. Intelligence Advanced Research Projects Activity (IARPA). The research is led by Tai Sing Lee, a professor in the Computer Science Department and the Center for the Neural Basis of Cognition (CNBC).

The research effort, through IARPA’s Machine Intelligence from Cortical Networks (MICrONS) research program, is part of the U.S. BRAIN Initiative to revolutionize the understanding of the human brain.

A “Human Genome Project” for the brain’s visual system

“MICrONS is similar in design and scope to the Human Genome Project, which first sequenced and mapped all human genes,” Lee said. “Its impact will likely be long-lasting and promises to be a game changer in neuroscience and artificial intelligence.”

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Researchers at the University of Bristol have created ‘Mogrify’ — an algorithm that can predict how to reprogram virtually any type of cell

One way of creating new cells is with stem cells. The most famous of these are embryonic and induced pluripotent stem cells, the latter made from your own cells. While these cells have immense potential, the process of creating them is complicated and not without error. Coaxing these cells into a new type once you’ve made them is also easier said than done.

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Another article just came out today providing additional content on the Quantum Computing threat and it did reference the article that I had published. Glad that folks are working on this.

The NSA is worried about quantum computers. It warns that it “must act now” to ensure that encryption systems can’t be broken wide open by the new super-fast hardware.

In a document outlining common concerns about the effects that quantum computing may have on national security and encryption of sensitive data, the NSA warns that “public-key algorithms… are all vulnerable to attack by a sufficiently large quantum computer.”

Quantum computers can, theoretically, be so much faster because they take advantage of a quirk in quantum mechanics. While classical computers use bits in 0 or 1, quantum computers use “qubits” that can exist in 0, 1 or a superposition of the two. In turn, that allows it to work through possible solutions more quickly meaning they could crack encryption that normal computers can’t.

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Nice! Robo-advice will be accessed by investors worth $2.2 trillion by 2020, equivalent to 12% of the global retail funds.

If you’re a finance professional, the question you probably get asked most by your friends and acquaintances is “what investments they should make”? That’s the basic question that everyone with money will ask. They may ask the “financial advisor” at their bank, they may turn to Google for advice, they may ask their “friends who work in finance”, or they may listen to recommendations of people they trust. However, individuals with a high net worth will typically seek out a wealth management firm with a brand they trust. But which firm?

Try to Google “top wealth management firms” and the first 5 search results will be a comparison of the top 100 wealth management firms. That’s a very competitive space. How do you differentiate yourself from your 99 competitors who are essentially trying to so the same thing you are? One way is through the use of technology, and as a result we see the rise of “robo advisors”. Here’s the definition of a “robo-advisor” from Investopedia:

A robo-advisor is an online wealth management service that provides automated, algorithm-based portfolio management advice without the use of human financial planners. Robo-advisors use the same software as traditional advisors, but usually only offer portfolio management and do not get involved in more personal aspects of wealth management, such as taxes and retirement or estate planning.

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The game of Go has long been viewed as the most challenging of classic games for artificial intelligence due to its enormous search space and the difficulty of evaluating board positions and moves.

Google DeepMind introduced a new approach to computer Go with their program, AlphaGo, that uses value networks to evaluate board positions and policy networks to select moves. These deep neural networks are trained by a novel combination of supervised learning from human expert games, and reinforcement learning from games of self-play. Without any lookahead search, the neural networks play Go at the level of state-of-the-art Monte-Carlo tree search programs that simulate thousands of random games of self-play. DeepMind also introduce a new search algorithm that combines Monte-Carlo simulation with value and policy networks. Using this search algorithm, our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.

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