Seeing how AI programmes use algorithms to interpret images, and replicate our human connection in the modern world: that's the premise of Tate Britain's new exhibition, Recognition. The exhibition uses artificial intelligence technologies (such as object and facial recognition) to pair photos from news agency Reuters with paintings from the Tate collection. Visitors are able to view the virtual gallery created by the programme, and learn about why it chose each specific art/photo match, as well as share their favourite choices made by the machine. They can also help out the AI by making their own comparisons between Reuters' real-time news images and the museum's archives. The aim of the project is to find out whether, over the course of three months, the AI programme can learn and improve on its pairings, using its own observations as well as the input of Tate visitors.

These are the questions I often meet in my daily work with innovative startups and corporates all trying to assess the trajectories with higher potentials and market opportunities. AI is a true game changer. It has showed extraordinary advancements in the last couple of years thanks to a new technique called "deep learning" which allows machines to become superintelligent by crunching myriads of examples and data rather than being explicitly programmed. Independently of the specific deep learning technique, the idea of taking advantage from extensive amounts of computing power and giant quantities of data to mimic human brains and neuronal systems is already proving very effective to power internet search engines, block spam emails, translate web pages, recognize voice commands, etc. Moreover the recent success of AI is opening the route to applications in a number of industries: improved vision systems for example are up to boost disruption in the automotive and mobility worlds with self-driving cars, retailers logistics with smarter delivery drones, the surveillance universe and medical diagnosis scientific breakthroughs with powered image recognition. In addition dynamic-reaction systems and Automated Teller Machines can be used in massive clusters of white-collar jobs also in traditionally conservative industries such as in legal, banking, health-care, journalism, etc., sectors where human answers, expert advice, reports, editorial summaries can be substituted by Intelligent Machines.

Why we urgently need to measure AI's societal impacts How will artificial intelligence systems change the way we live? This is a tough question: on one hand, AI tools are producing compelling advances in complex tasks, with dramatic improvements in energy consumption, audio processing, and leukemia detection. There is extraordinary potential to do much more in the future. On the other hand, AI systems are already making problematic judgements that are producing significant social, cultural, and economic impacts in people's everyday lives. AI and decision-support systems are embedded in a wide array of social institutions, from influencing who is released from jail to shaping the news we see.

It's been a busy summer in the artificial intelligence (A.I.) space, but the most interesting A.I. opportunities might not be coming from the biggest names. You may have heard about Tesla's self-driving cars that made headlines twice, for vastly different reasons -- a fatal crash in Florida in which the driver was using the Autopilot software, and claims by a Missouri man that the feature drove him 20 miles to a hospital after he suffered a heart attack, saving his life. Or you might have heard of Apple spending 200 million to acquire machine learning and A.I. startup Turi. A smart drone defeated an experienced Air Force pilot in flight simulation tests. IBM's Watson diagnosed a 60-year-old woman's rare form of leukemia within 10 minutes, after doctors had been stumped for months.

Lately, there has been a lot of buzz about Chatbots – applications that make use of a conversational user interface instead of a graphical interface that has been the norm in the past decade or three. With any buzz-worthy technology, there is the usual confusion about what exactly is a Chatbot – and depending on how many articles you have read, it is anywhere between a simplified IVR or the technology which will result in world peace. Neither Chatbots nor Conversational User Interfaces are new. All businesses must think whether they need to have a strategy to have a "Conversational App" – just like they all had to for mobile apps. Bot-directories on the Internet are already full of shallow and meaningless Chatbots, just like app stores were when mobile apps became all the rage.

When it comes to enterprise customer care, machine learning enables virtual assistant solutions to automate tasks that used to require a live agent: password resets, address and complex information collection, even sales support. Integrating machine learning into enterprise customer care opens doors to more flexible automated solutions. It also frees up live agents to focus on handling complex or revenue-generating tasks. With the growing challenges and volume of customer interactions that most companies must handle, that flexibility, efficiency, and accuracy is exactly what's needed. With a little help and vetting from the IT department, the contact center can take enterprise customer care to the next level. Of course, success comes with a cost.

We can see a few interesting things in this table. In a similar talk by Owen at ODSC Boston 2015 titled "Open Source Tools and Data Science Competitions", he again summarized common parameters he uses: We can see some minor differences that may be relevant. Finally, Abhishek Thakur, in his post titled "Approaching (Almost) Any Machine Learning Problem" provided a similar table listing out key XGBoost parameters and suggestions for tuning. The spreads do cover the general defaults suggested above and more. It is interesting to note that Abhishek does provides some suggestions for tuning the alpha and beta model penalization terms as well as row sampling. You can develop and evaluate XGBoost models in just a few lines of Python code.

Xperilabs is a machine learning, data science and algorithms studio for Indian Financial Markets. We are currently working in stealth mode, rapidly developing the pre-beta version of our platform. Our vision is to build a state-of-the-art platform that offers tools and algorithms that will allow retail as well as institutional investors to explore and design quantitatively driven financial algorithms.

The short answer: To make their lives easier. Marketers have depended on tools and technology to automate their work and reduce manual effort for a while. Yet, there has always been a gap in terms of effort and quantifiable results. Intuition on the right audience and time to send messages aren't enough to answer a digital marketer's basic questions. Who should I reach out to? What should I send?

Spending thousands of hours playing Grand Theft Auto might have questionable benefits for humans, but it could help make computers significantly more intelligent. Several research groups are now using the hugely popular game, which features fast cars and various nefarious activities, to train algorithms that might enable a self-driving car to navigate a real road. But the stunningly realistic scenery found in Grand Theft Auto and other virtual worlds could help a machine perceive elements of the real world correctly. A technique known as machine learning is enabling computers to do impressive new things, like identifying faces and recognizing speech as well as a person can. But the approach requires huge quantities of curated data, and it can be challenging and time-consuming to gather enough.