Machine learning is going through something of a renaissance these days. It seems like there are new moves forward with this technology every day, from advances in image and sound recognition to lip reading and beating us at all the games. However, this renaissance has largely been funded by Silicon Valley. Companies are scrambling to find enough programmers capable of coding for ML and deep learning. Last year was a good year for the freedom of information, as titans of the industry Google, Microsoft, Facebook, Amazon, and even Baidu open-sourced a number of their ML frameworks. Freeing code is a great way to attract talent and grow a community, as well as garner good will. Google is unquestionably the goliath in the field of open-source machine learning with TensorFlow beating all comers by most metrics.
On Wall Street, blockchain could upend how institutions trade with one another. One example: It could shrink the three days that it currently takes to clear a securities transaction into seconds. It could also enable entirely new forms of exchange — think self-enforcing contracts and, yes, digital currency. Indeed, “blockchain will do for transactions what the internet did for information,” IBM CEO Ginni Rometty said at a conference in Geneva in September. Extending Rometty’s analogy, it should be noted that it’s early days for blockchain, with developers still establishing the ground rules for the equivalents of the TCP/IP language protocols that allowed the internet to become the internet. But despite all the anarchistic rumblings that the end is nigh for Wall Street intermediaries, here’s the surprising reality
CMOs are nearly twice as likely as CIOs to lead digital transformation efforts within their organizations, according to new research from Altimeter Group. The top three transformative initiatives — accelerating innovation, modernizing IT infrastructure and improving operational agility — typically fall under the responsibility of IT, but a disconnect exists between the trends driving change and the individuals who lead the efforts, according to Brian Solis, principal analyst at the research and advisory firm. CIOs are more likely sit on the sidelines, because their agendas are already full, he says. When CIOs join an organization there's usually a backlog of demanding projects they need to take over, according to Solis. "There's an aspect of being in IT that is always looking in the past, or at least working in the past," he says.
"You get all these wonderfully smart people into a room, and what happens is you end up in this performance environment ... you want to perform well, so you grind and grind," he explained. "But what you really want to create as a leader is a learning environment. If you're in a performance environment, you're not going to do a lot of learning, because you're always on." Second, Sakaguchi said, team leaders need to model curiosity and ask questions. He explained that since he does not have as strong a background in software development as many on his team, he often asks questions that some might consider "dumb" questions in front of his team members. But instead of being looked down upon, Sakaguchi said his team often appreciates the fact that he asked the question.
Truly, the importance cannot be overstated. Enterprises are beginning to adopt chatbot platforms in the same way they are currently embracing mobile and IoT platforms, and that number is expected to grow exponentially. App downloads are slowing, and messaging platforms have proven their staying power. Customers and employees on interoffice messaging platforms like Slack — who, by the way, just invested $80 million in chatbots for their platform — use messaging for the same reasons: It’s monumentally convenient for the user, incredibly cost- effective, and gets results faster. Today’s enterprise chatbots are comprehensive toolsets that every company needs if they want to compete. Chatbots can handle complex multi-step workflows, answer questions, and even make software platforms easier to use, giving them more value to your users.
There is a handful of popular deep learning libraries, including TensorFlow, Theano, Torch and Caffe. Each of them has Python interface (now also for Torch: PyTorch). So, which to choose? First, as always, screw all subtle performance benchmarks, as premature optimization is the root of all evil. What is crucial is to start with one which is easy to write (and read!), one with many online resources, and one that you can actually install on your computer without too much pain. Bear in mind that core frameworks are multidimensional array expression compilers with GPU support. Current neural networks can be expressed as such. However, if you just want to work with neural networks, by rule of least power, I recommend starting with a framework just for neural networks. For example…
The compute stick, a standard USB 3.0 drive, is among a series of AI hardware implementations and development tools in Intel’s pipeline. The heart of the USB-based device is the Movidius Myriad 2 vision-processing chip capable of handling more than 100 gigaflops within a 1-watt power envelope. The ability to run real-time deep learning networks from the device “enables a wide range of AI applications to be deployed offline,” explained Remi El-Ouazzane, vice president and general manager of Movidius, the computer vision startup Intel acquired last September. The device converts convolutional neural networks into an embedded neural network running atop the Myriad VPU. A tuning feature allows developers to validate scripts to compare accuracy of customized models to the original. The device can then be used as a neural network accelerator that adds deep learning inference capabilities, the company said.
Evolutionarily, it is far more important to be able to concentrate on movement within a scene than to take repeated, indiscriminate inventories of its every detail. This becomes especially relevant when we are talking about the vast amounts of data being captured and analyzed in certain applications and use models – autonomous cars, for example. In controlled environments, sophisticated post-processing can deal with this limitation of traditional video imaging. But this brute-force approach simply won’t work in real-time – in-the-field use cases with limited power, bandwidth, and computing resources, including mobile devices, drones, or other kinds of small robots. ... Rather than analyze images on a frame-by-frame basis (our eyes certainly do not do this), the new paradigm is based on selectively capturing visual information according to changes in the scene.
AI is actually more pervasive now than most people think, and as computer systems have become more advanced, the use of machine learning algorithms has become more common. The problem is that the same smart technology can be used to undermine these systems. “Computer security is definitely moving toward machine learning,” Google Brain researcher Ian Goodfellow told the MIT Technology Review. “The bad guys will be using machine learning to automate their attacks, and we will be using machine learning to defend.” Training AI to fight malicious AI is the best way to prepare for these attacks, but that’s easier said than done. “Adversarial machine learning is more difficult to study than conventional machine learning,” explained Goodfellow. “It’s hard to tell if your attack is strong or if your defense is actually weak.”
More often than not, when I meet fellow engineers, thought leaders or young job aspirants, engineering culture is one hot topic that invariably pops up for discussion. Unfortunately, this is one area that lingers in the backdrop when business focus areas are defined. Most of you would agree, no matter how vehemently we convey this across, the topic fails to attract the attention it deserves, until we retrospect sitting on a large pile of issues to be solved. ... No matter where we are based or which industry we belong to, hiring good engineers has always been a challenge. The effort is worth it when these awesome engineers help build our engineering brand. An established engineering brand results in attracting more such talent. As great sustainable culture is usually built bottom-up, it is imperative that we hire the best.
Quote for the day:
"What I've really learned over time is that optimism is a very, very important part of leadership." -- Bob Iger