Statistical AI (ie machine learning) is capable of mimicking the brain's pattern recognition skills but is garbage at applying logic. Symbolic AI, on the other hand, can leverage logic (assuming it's been trained on the rules of that reasoning system), but is generally incapable of applying that skill in real-time. But what if we could combine the best features of the human brain's computational flexibility with AI's massive processing capability? That's exactly what the team from DeepMind recently tried to do. They've constructed a neural network able to apply relational reasoning to its tasks. It works in much the same way as the brain's network of neurons. While neurons use their various connections with each other to recognize patterns, "We are explicitly forcing the network to discover the relationships that exist" between pairs of objects in a given scenario
Relevant IT know-how can dramatically change board discussions and perspectives. Some boards employ external experts or consultants to meet this need, but outsourcing this function is an approach that can frequently lack accountability, eschew specific business context, disregard the organization’s technology capabilities, or rely on generic recommendations. Boards can address this deficiency through a three-pronged approach: appointing a business-savvy technologist to the board, taking a more offensive technology position, and considering a technology committee. Although current or former CIOs, CTOs, CISOs, and other C-level technology leaders could provide valuable input and perspective to boards, Deloitte’s analysis indicates that only 3 percent of all public companies appointed a technologist to newly opened board seats in 2016
According to Dr. von Bieren, X-Spect smart homes technology is similar to that of the SCiO scanner. Like the SCiO technology, it can only read the ingredients of orange, not a cupcake. So only homogenous food items need apply. According to c|net, the most unique thing about X-Spect scanner is its ability to read the makeup of fabric and stains. Rich Brown of c|net wrote, “Right now the X-Spect can determine up to two component materials in a piece of fabric, and also let you know their relative proportions. We saw a demo on a blended cotton-poly t-shirt, for example. The creators hope to bring X-Spect to the point where it can read three different materials. It’s not there yet, but it can read four different kinds of stains. I saw it read chocolate and lipstick via a prepared demo at the Bosch booth.”
Endgame’s Alexa integration — which they believe is a first in the security industry —utilizes natural language understanding to let security analysts simply ask their network what’s going on. They can ask anything from a general check-in to specific queries about attack types, and execute commands to keep their system safe. The idea is that junior analysts can sit, ask questions, and take actionable steps without being crippled because of syntax or query language. "We wanted to tackle the problem of learning language," Filar said. "It's a good way to help move up to a senior analyst more quickly." Though, I did wonder how it would be possible to move up to a senior analyst without learning the programming language. "What we try to do," Filar said, "is provide a framework that can grow with the experience of the analyst.
This will be the fourth feature update to Windows 10 in a little over two years. And that pace will continue, with new feature updates (essentially full upgrades) due on a predictable twice-yearly cadence going forward. As with previous feature updates, there will be no last-minute surprises in this update. It's been developed in the open, with dozens of preview releases to members of the Windows Insider Program. For those who haven't been paying close attention, though, this article should get you up to speed quickly. When I looked through my notes from the past few months of testing Windows Insider builds, I was struck by how many changes have made their way into this update. And those changes encompass a wide array of user scenarios, including a healthy assortment aimed at IT pros and developers.
Deep learning is slowly becoming part of the class of algorithms data scientists need to know about. Originally used in computer vision and speech recognition, there are starting to be examples and use cases involving data types and problems that data scientists can relate to. Challenges include choosing the right network architecture, hyperparameter tuning, and casting problems and transforming data so they lend themselves to deep learning. In many cases, users prefer and favor models that are explainable. Given that their underlying mechanisms are somewhat understandable, explainable models are also potentially easier to improve. With the recent rise of deep learning, I’m seeing companies use tools that explain how models produce their predictions and tools that can explain where a model comes from by tracing predictions from the learning algorithm
Technology provides significant and material financial incentives over its unpredictable and fallible human counterparts. Perhaps most tellingly, automation is a key component of most vendors’ ROI stories, meaning it’s a powerful tool in the “buy our product and we will save you money” toolbox. But should organizations really be sprinting headlong into automation? There is no question that automation delivers significant value to organizations. Repetitive and boring tasks waste valuable time and result in unhappy and unengaged employees. ... Implementing some automated solutions can prove valuable. However, when it comes to network security, fully automating the tasks of a security analyst can be a dangerous and foolish decision for a variety of reasons.
What’s emerging is microservices – replacing monolithic applications with modular applets that are designed to do specific tasks. While the overall functionality may be the same as the monolithic app it replaces, application maintenance overhead is drastically reduced. It also means new technologies can easily be integrated, and emerging technologies like IoT and the Industrial Internet can become part of the ecosystem. The end result? Microservices architectures creates agility. So why isn’t this happening quickly? Business response to demands for new apps, integrations, business models, and emerging technologies is slow and inefficient because existing IT infrastructure, coupled with the legacy (monolithic) application model, are obstacles to the rapid and scalable delivery of digital transformation initiatives including mobile, cloud and IoT.
Having a human in the loop doesn’t always stop AI going wrong, however. When Evans tried the same approach with the game Road Runner, the AI overseer wasn’t able to block every big mistake the game-playing AI made. More complicated Atari games would require years of human oversight before agents were able to play without making mistakes. Even a system trained with human oversight is never going to be absolutely safe. It’s hard to know how these systems will behave in circumstances that an AI hasn’t been trained to handle, says Evans. And even the best AI could be led astray by a sloppy human trainer. “This is only as good as the human,” says Evans. If we are to trust robots in the home and hospitals, then we will need to have some guarantees about their safety, says David Abel at Brown University in Providence, Rhode Island.
Perry was surprised to see how many organizations more closely evaluate their mix of on-premises and off-premises compute resources and spending. CI works well in environments with small staff sizes. But growing enterprises still need data center specialists, particularly within companies reluctant to move new workloads to the public cloud. ... Enterprises still need data center specialists in virtualization and, out of this rank, will come specialists in container management as enterprises adopt this technology, Perry said. Applications and software specialists are also in demand, particularly in growing areas such as security and DevOps, Perry said. A generalist position merges traditional specialist roles, such as server administrator or virtualization administrator, particularly if they involve management of CI. To successfully oversee CI, they must understand the storage ecosystem, Perry said.
Quote for the day:
"Leadership matters more in times of uncertainty." -- Wayde Goodall