Teachers are not ends-in-themselves, they are always a means to an end - improvements in the learner. Given this premise, could it be possible to eventually replace teachers with AI technology? This may not happen soon but let's, as a thought experiment, ask whether it could. Obvious points are that AI is 24/7, fast, scalable and cheaper. This gives it a head start.

So Brexit turned out to be the non-event for markets we expected with asset prices booming to record highs while support for remaining inside the single currency free-trade zone has risen within other European Union countries. As is often the case, the trade was to "fade" – or bet against – the apocalyptic hyperbole both before and after the referendum. Remarkably one boutique Australian quant shop did exactly this by leveraging academic studies of "blue green algae", which helped it generate enormous 32 per cent returns on the day of the vote. Taaffeite Capital Management (TCM) claim their "artificial intelligence" (aka computer code operating autonomously of humans) figured out that there were massive financial market mispricings that warranted shorts on equities and long positions on government bonds without actually knowing that a referendum was being held. The intellectual property underlying this "systematic" – or automated – trading strategy belongs to Massachusetts Institute of Technology PhD Desmond Lun, who is a 36-year old Australian professor of computer science at Rutgers University in the US, and a Melbourne University alumnus.

In the ever-changing world of high-tech gadgets and gizmos, a whole load of jargon is thrown our way that many of us don't necessarily understand. In our regular series, What is…, we'll tackle a tech term and explain what it means so you can understand it a bit more. Here we explain Artificial Intelligence, a developing computer science which is causing some controversy. In simple terms, Artificial Intelligence (AI) is enabling computers to do things like humans, especially the ability to think, learn and react like we do. AI was once something only imaginable in sci-fi movies, but in recent years it has increasingly become a reality. AI doesn't necessarily have to relate to robots either – it can be used as a tool within an array of IT systems.

If J.K. Rowling was ever concerned that artificial intelligence could do her job for her, she let out a sigh of relief this week. A fan of the Harry Potter series in San Francisco had an artificial intelligence computer algorithm write a few chapters for the iconic series, after teaching the computer to read earlier novels. The results are far from ideal. Max Deutsch, a product manager at Intuit, trained a a Long Short Term Memory (LSTM) Recurrent Neural Network computer by teaching it to read the first four Harry Potter novels. A LSTM computer is trained to notice patterns (say, in genomes or handwriting), which makes it a great test subject for writing patterns.

Legion Analytics is looking to make your sales team more productive with the help of artificial intelligence. We covered the startup last year when it was part of Y Combinator's fellowship program and offered to help companies find sales leads. CEO Jamasen Rodriguez told me that the company's vision has expanded -- it's not just focused on lead generation anymore, but rather becoming "a full-stack sales company." And part of that vision involves automating the mundane parts of the sales process. Bots are a pretty trendy topic right now, and Legion Analytics' claim of using natural language and machine learning technology is pretty familiar, too.

WALTHAM, MA--(Marketwired - Jun 23, 2016) - CloudLock, the leading provider of Cloud Access Security Broker (CASB) and Cybersecurity-as-a-Service solutions, today announced the release of the next generation of its innovative machine learning capabilities to include suspicious login activity monitoring, location-based anomaly detection, and IP reputation analysis to identify anomalies, zero in on suspicious behavior, and pinpoint true threats across SaaS, IaaS, PaaS, and IDaaS cloud platforms. The inability to detect real threats from millions of alerts they receive daily as well as the lack of timely response capabilities are the greatest challenges facing security teams today. Pioneered by CloudLock's research intelligence arm, the CyberLab, machine learning capabilities are the foundation of the Cloud Security Fabric, helping security teams narrow their focus on user activities indicative of true threats. Using the company's Cloud Threat Funnel methodology, along with big data technologies and multiple advanced clustering algorithms, CloudLock's machine learning technology continuously evolves based on analyzing the industry's largest data set spanning over one billion files and events monitored daily. CloudLock's expanded machine learning capabilities include: Suspicious Login Activity Monitoring captures high frequency login anomalies, such as login failures and login challenges from unusual devices, geographies and time periods for a given user, indicate potential threats to corporate user accounts.

Palantir in particular is interesting because of their B2B business model and their lack of traditional marketing or sales teams1.] They helped convict Bernie Madoff 2, analyzed roadside bomb patterns in Afganistan, and are rumored to have helped locate Osama bin Laden 3. They've worked with the FBI, CIA, Marine Corps, Air Force, and at least 8 other government organizations 4. With that sort of pedigree, you'd think Palantir was some sort of top-secret government program. Complete with free meals and gym memberships, Palantir is actually one of the most successful privately-held startups in Silicon Valley. Started by a group of former PayPal people about a decade ago, Palantir is now worth 15 billion 5. They focused on one core product – machine augmented6 data analysis – and built an almost-exclusively engineering team to make it happen.

He was talking about 4 ways we can improve user experience using algorithms and machine learning. In all of these Giles was emphasizing the role of the algorithm, which like a cog sits between collecting the raw data and spitting out the extracted, packaged up information for the user to act upon. One of the points Giles made was around finding the right balance between the complexity of data needed for analysis. Complexity time and effort so you end up looking for the sweet spot of minimum data to get the desired information. Another point was to decide between: - High bias -- you aim to be precise, but if you get it wrong it is noticeably wrong.

When it comes to drones, AT&T wants to be in the driver's seat. The massive U.S. carrier is already using drones to inspect its cell towers and may someday put cells on drones to boost service at big events. But it's also eyeing a major role in the way others use drones. At the heart of it all is AT&T's network, technology executives from the company said Friday at AT&T's Shape conference in San Francisco. They see the network as a future backbone for command and control of drones or even a drone traffic management system. Air traffic control is one of the big challenges looming over the future of commercial and recreational drones.

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