Complex interdependencies between applications and computing environments can make whitelist-based security nearly impossible to enforce in practice, a Cisco executive has argued as the company debuts a data-centre analytics platform that utilises machine-learning techniques to model and predict the security impact of configuration and application changes. Those changes can wreak havoc with conventional security models built around blacklists that are difficult to keep up to date as the environment changes, vice president of product marketing Rajeev Bhardwaj told CSO Australia as the company debuted a Tetration Analytics platform that surveils the data centre to monitor data flows and application interactions. While many data-centre operators preferred to operate on a zero-trust, whitelist-based security model, that had been difficult to accomplish because of changing interdependencies within the "black box" of the data centre, Bhardwaj said. "The data centre of today has multiple layers of complexity from compute, network, and storage infrastructure as well as virtualisation, firewalls, load balancers and customer applications," he explained. "If something goes wrong it's extremely hard to find out what happened: you look at the black box and don't know which application is talking with which applications, which ports are open, which applications are protected with a firewall."

Building such a robot isn't just a way of getting rid of household chores, according to a blog entry posted by the nonprofit research group. It would also be a neat way of testing whether or not its work in artificial intelligence is progressing in the right way. There are already ways of creating a robot that can carry out specific tasks, the researchers note. The difference is that Musk's team hopes to create "learning algorithms" that would allow the creation to serve as a "general purpose" robot – meaning that it can be left around the home and be clever enough to work out what it needs to do to clean.

Looking back is much easier than looking forward - when Management Today published its first issue in 1966, Britain was debating decimalisation, the average weekly wage was 14 and workers were enjoying a'golden age' of employment and easy-to-find jobs. As our Future of Work special report has demonstrated, we've come a long way since then, but it's safe to say that the changes over the next 50 years will be ever more profound. Of course, no one can predict them with any accuracy - the future is not a mere extrapolation of the past. But each year CEBR, one of the UK's leading economics consultancies, predicts where the British economy will rank in 15 years' time. As things stand, it forecasts that in 2031 the UK will be the world's sixth biggest economy with a GDP of 4.7bn (compared to 3bn now), and that it could even overtake the German and Japanese economies during the 2040s.

The man has built vehicles and rockets. Up next is domestic robots. Elon Musk, the CEO of Tesla Motors and SpaceX, has initiated the startup OpenAI which will build robots meant to function inside the home environment. By creating a robot from scratch, the machine will be thoroughly tested which is a good thing in the long run. How a machine is able to perform ordinary activities is what is at stake here.

The idea behind app Nexar is ambitious: create a type of air-traffic control system for the road. Catalogue the cars in a given location, track dicey behavior and help drivers avoid dangerous intersections and even bad drivers. Nexar is a free dashcam app that, using artificial intelligence, creates a database of an area's roads and drivers. If car stops short in front of you, even a few cars in front of you, Nexar's database can send you a notification so you'll have more time to brake. If an accident happens around the corner and cars are getting detoured onto your route, the app will tell you.

Transportation of property for compensation or hire allowed provided that The aircraft, including its attached systems, payload and cargo weigh less than 55 pounds total; The flight is conducted within visual line of sight and not from a moving vehicle or aircraft; and The flight occurs wholly within the bounds of a State and does not involve transport between (1) Hawaii and another place in Hawaii through airspace outside Hawaii; (2) the District of Columbia and another place in the District of Columbia; or (3) a territory or possession of the United States and another place in the same territory or possession. If a company or person wants to fly a drone in a way different from these rules, they can do so by applying to the FAA for a Certificate of Waiver, which if granted will give them a legal exception. The rules are a major step towards clarity in the vague world of drone law, though I'm certain there is much still to be decided and discovered in the full body of the rule. Here, if you wish to delve through it, is the full rule.

Machine Learning is something with immense potential and a lot of applications in almost any field but needs a lot of knowledge for entry into the field. It's been a while since I started working on ML. My learning was not academic but a fast paced one to solve business problems. Here's some of the problems which I faced (or might have) and the solutions which I think would help reduce someone's struggle.

While Gupta might have been relieved that he was not taken to task by his stern coach at Prepathon, an online test prep startup founded in September 2015, what he didn't know was that the person he was chatting with on the Prepathon app was not his coach but a bot! Millennials - those born between 1980 and 2000 - are texting more and talking less. And millennials matter because they will account for 40% of all consumers in the US by 2020, as projected by Goldman Sachs. While Jaiswal might be content with one chatbot, others want multiple bots. Aarti Gill is one of them. The cofounder of FitCircle, a chat-based health and fitness platform, boasts 32,000 bot users.

Young people will bear the brunt of Artificial Intelligence (AI) fuelled job losses as smart systems undercut entry-level roles in everything from marketing to retail. Machine learning and expert systems will not destroy jobs wholesale, predicts George Zarkadakis, digital lead at advisory firm Willis Towers Watson, but will remove the need for many tasks that employees have traditionally cut their teeth on at the beginning of their careers. Zarkadakis cited a study by consultants McKinsey, which found that just under one third of activities that make up 60 percent of existing jobs will be automated. Unfortunately for new entrants to job markets, the bulk of these activities will be concentrated in starter roles, said Zarkadakis. "We've done some research ourselves and looked at the impact on entry-level jobs. Jobs that graduates get once they leave university. We found that many of the entry-level jobs are very susceptible to complete obliteration," he told The AI Summit in London.

For most, the term Artificial Intelligence evokes thoughts of futuristic technologies, of C-3PO and Asimov's "I,Robot." We think of walking, sentient robots that look like us – or at least we imagine they appear like us. What we don't generally think of is a glorified speakerbox, or a supercomputer tasked to create recipes. But that's where AI starts – with small steps and incremental advances in technology. These small steps gradually lead to giant leaps forward; think of a narrow canyon off in the distance: you're pretty sure you can jump the canyon, but first you need to get there.