Nice — Bridgewaters engaged. Actually, not too surprised by this.
IBTimes UK spoke to AI finance startup Walnut Algorithms about machine learning and the financial sector.
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.”
Continue reading “NSA Plans to ‘Act Now’ to Ensure Quantum Computers Can’t Break Encryption” »
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:
Continue reading “The Real Truth Behind the Rise of Robo-Advisors” »
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.
Google has achieved one of the long-standing “grand challenges” of AI, building a computer capable of beating expert players of the board game Go.
In a paper published in Nature on 28th January 2016, we describe a new approach to computer Go. This is the first time ever that a computer program “AlphaGo” has defeated a human professional player.
The game of Go is widely viewed as an unsolved “grand challenge” for artificial intelligence. Games are a great testing ground for inventing smarter, more flexible algorithms that have the ability to tackle problems in ways similar to humans. The first classic game mastered by a computer was noughts and crosses (also known as tic-tac-toe) in 1952. But until now, one game has thwarted A.I. researchers: the ancient game of Go.
Continue reading “Google DeepMind: Ground-breaking AlphaGo masters the game of Go” »
An international team of researchers has developed a new algorithm that could one day help scientists reprogram cells to plug any kind of gap in the human body. The computer code model, called Mogrify, is designed to make the process of creating pluripotent stem cells much quicker and more straightforward than ever before.
A pluripotent stem cell is one that has the potential to become any type of specialised cell in the body: eye tissue, or a neural cell, or cells to build a heart. In theory, that would open up the potential for doctors to regrow limbs, make organs to order, and patch up the human body in all kinds of ways that aren’t currently possible.
It was Japanese researcher Shinya Yamanaka who first reprogrammed cells in this way back in 2007 — it later earned him a Nobel Prize — but Yamanaka’s work involved a lot of labourious trial and error, and the process he followed is not an easy one to reproduce. Mogrify aims to compute the required set of factors to change cells instead, and it’s passed its early tests with flying colours.
Understanding time is one of the big open questions of physics, and it has puzzled philosophers throughout history. What is time? Why does it appear to have a direction? The concept is defined as the “arrow of time,” which is used to indicate that time is asymmetric – even though most laws of the universe are perfectly symmetric.
A potential explanation for this has now been put forward. Physicist Sean Carroll from CalTech and cosmologist Alan Guth from MIT created a simulation that shows that arrows of time can arise naturally from a perfectly symmetric system of equations.
The arrow of time comes from observing that time does indeed seem to pass for us and that the direction of time is consistent with the increase in entropy in the universe. Entropy is the measure of the disorder of the world; an intact egg has less entropy than a broken one, and if we see a broken egg, we know that it used to be unbroken. Our experience tells us that broken eggs don’t jump back together, that ice cubes melt, and that tidying up a room requires a lot more energy than making it messy.
A new algorithm has been developed that will drastically reduce the time and effort needed to create induced pluripotent stem cells (iPSCs). As a result of this breakthrough, we can expect a dramatic revolution in regenerative medicine in the near future.
What if you could directly reprogram cells to develop into whatever you wished? What if you could take an undifferentiated, incipient cell, full of the unrealized potential to become any one of the many specialized cells in the human body, and nudge it into becoming ocular tissue, or neural cells, even a new heart to replace an old or damaged one?
This is the promise afforded by Mogrify, the result of the application of computational and mathematical science to the problems of medicine and biology. It was developed by an international collaboration of researchers from the Duke-NUS Medical School in Singapore, the University of Bristol in the United Kingdom, Monash University in Australia, and RIKEN in Japan. The new research was published online in the journal Nature Genetics.
I have seen this model so many times over the decades. And, I even was engaged in some of these experiments in the past. The continued problem we saw is “subjective reasoning” by humans which makes the experiments flawed.
And, as Yampolskiy suggested this is not true AI; it is using human insights and identifying patterns based on human input whch also includes subjective reasoning.
While AI focuses on creating intelligent machines that perform human tasks, a human-based algorithm, harnessing the power of the crowd to make predictions, shows remarkable accuracy.
