I like to watch long-term technology and business trends and watch as they shape the products and services that I get to use and to write about. As I was preparing to write today's post, three such trends came to mind: As the industry pushes forward in accord with these trends, a couple of interesting challenges have surfaced over the past decade or so. Again, here's a quick list (yes, I do think in bullet points): The GPU (Graphics Processing Unit) was born of these trends, and addresses many of the challenges! Processors have reached the upper bound on clock rates, but Moore's Law gives designers more and more transistors to work with. Those transistors can be used to add more cache and more memory to a traditional architecture, but the von Neumann Bottleneck limits the value of doing so. On the other hand, we now have large markets for specialized hardware (gaming comes to mind as one of the early drivers for GPU consumption).

Computers can already recognize you in an image, but can they see a video or real-world objects and tell exactly what's going on? Researchers are trying to make computer video recognition a reality, and they are using some image-recognition techniques to make that happen. Researchers in and outside of Google are making progress in video recognition, but there are also challenges to overcome, Rajat Monga, engineering director of TensorFlow for Google's Brain team, said during a question-and-answer session on Quora this week. The benefits of video recognition are enormous. For example, a computer will be able to identify a person's activities, an event or a location.

Over 500 founders, CTOs, business leaders, software engineers & entrepreneurs came together at the 2nd annual Deep Learning Summit in London, to explore how deep learning will impact communications, manufacturing, healthcare, transportation and more. This event took place alongside the Deep Learning Summit, view the Storify: goo.gl/pCcQ33

Summary: What comes next after Deep Learning? How do we get to Artificial General Intelligence? Adversarial Machine Learning is an emerging space that points to that direction and shows that AGI is closer than we think. Deep Learning, Convolutional Neural Nets (CNNs) have given us dramatic improvements in image, speech, and text recognition over the last two years. They suffer from the flaw however that they can be easily fooled by the introduction of even small amounts of noise, random or intentional.

SAN FRANCISCO: Major technology firms have joined forces in a partnership on artificial intelligence, aiming to cooperate on "best practices" on using the technology "to benefit people and society." Microsoft, Amazon, Google, Facebook, IBM, and Google-owned British AI firm DeepMind on Wednesday announced a non-profit organization called "Partnership on AI" focused on helping the public understand the technology and practices in the field. The move comes amid concerns that new artificial intelligence efforts could spin out of control and end up being detrimental to society. The companies "will conduct research, recommend best practices, and publish research under an open license in areas such as ethics, fairness, and inclusivity; transparency, privacy, and interoperability; collaboration between people and AI systems; and the trustworthiness, reliability, and robustness of the technology," according to a statement. Academics, non-profit groups, and specialists in policy and ethics will be invited to join the board of the Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI).

Skymind, a company developing an open-source deep-learning library for Java, along with tools for implementation, today closed 3 million in financing from Tencent, SV Angel, GreatPoint Ventures, Mandra Capital and Y Combinator. Skymind was previously part of Y Combinator's Winter 2016 batch and has taken money from Joe Montana's Liquid 2 Ventures and a number of other prominent angels. Chris Nicholson, the company's co-founder and CEO, decided to start the company after he noticed the steady stream of deep-learning researchers leaving the halls of academia for the six- and seven-figure salaries of large tech companies. With human capital becoming a finite resource, the challenge quickly became about helping companies leverage existing resources to play in the world of deep learning. Eighty percent of the world's programmers are versed in Java programming.

Lots of prior work with hierarchy (hierarchical RNN / stacked RNN) and multi-scale (LSTM, clockwork RNN) but they all rely on pre-defined boundaries, pre-defined scales, or soft non-hierarchical boundaries. Avoids "soft" gating which leads to "curse of updating every timestep". Discrete (binary) decisions are difficult to optimize due to non-smooth gradients. Uses straight-through estimator (as an alternative to REINFORCE) to learn discrete variables. The simplest variant uses a step function on the forward pass and a hard sigmoid on backward pass for gradient estimation.

Introduction In the previous article, we described combat performance in the Artificial Intelligence PK Gold Medal Stenography Competition and told the story behind the annual Alibaba Cloud meeting's Man VS Machine competition. Are there any curious technology geeks out there? What was the on-site real-time transcription system? What on earth is the core of a speech recognition system? How come the Alibaba Cloud iDST speech recognition system is so accurate?

NEW YORK - 28 Sep 2016: Amazon, DeepMind/Google, Facebook, IBM (NYSE: IBM) and Microsoft today announced that they will create a non-profit organization that will work to advance public understanding of artificial intelligence technologies (AI) and formulate best practices on the challenges and opportunities within the field. Academics, non-profits, and specialists in policy and ethics will be invited to join the Board of the organization, named the Partnership on Artificial Intelligence to Benefit People and Society (Partnership on AI). Leading tech industry researchers from Amazon, DeepMind/Google, Facebook, IBM and Microsoft convened to announce a partnership on artificial intelligence (AI) best practices, at IBM's Watson headquarters in New York City, Weds., September 28, 2016. Founding members of the Partnership on Artificial Intelligence from left: Eric Horvitz, Microsoft; Francesca Rossi, IBM; Yann LeCun, Facebook and Mustafa Suleyman, Google/DeepMind. Not pictured is Ralf Herbrich, Amazon.

Fujitsu has introduced a new technology to improve the internal memory of GPUs in order to increase the machine learning accuracy. Fujitsu claims that the new technology has doubled the efficiency of machine learning compared with previous technology. According to the company, in recent years the use of GPUs in machine learning has increased. GPUs have been providing the raw power to complete the complex calculations required for machine learning. The calculations can be done seamlessly when the data is stored in the GPU chip, but there is a limit on the GPU chips to store information.