Artificial intelligence boosters predict a brave new world of flying cars and cancer cures. Detractors worry about a future where humans are enslaved to an evil race of robot overlords. Veteran AI scientist Eric Horvitz and Doomsday Clock guru Lawrence Krauss, seeking a middle ground, gathered a group of experts in the Arizona desert to discuss the worst that could possibly happen -- and how to stop it. Their workshop took place last weekend at Arizona State University with funding from Tesla Inc. co-founder Elon Musk and Skype co-founder Jaan Tallinn. Officially dubbed "Envisioning and Addressing Adverse AI Outcomes," it was a kind of AI doomsday games that organized some 40 scientists, cyber-security experts and policy wonks into groups of attackers -- the red team -- and defenders -- blue team -- playing out AI-gone-very-wrong scenarios, ranging from stock-market manipulation to global warfare.

Niederhoffer will be sitting on the panel Artificial Intelligence – should we unplug man from the machine? In his view, whilst it is clear that in some domains machine learning and artificial intelligence is starting to make a big difference, the key is understanding which domains are appropriate and which domains are potentially problematic. Some domains, like object and speech recognition, linguistic analysis, and credit analysis are perfect for machine learning and particularly, deep learning algorithms. But in Niederhoffer's experience, making short term market predictions using machine learning is perilous, though possible.

The following was published on CMSWire.com on Feb. 22, 2017. The argument against artificial intelligence (AI) is driven by fear. According to Stephen Hawkings we do have reason to beware of the consequences of the advancement of artificial intelligence, including the possibility of the end of the human race. The rise of the machines won't be happening imminently, after all AI is still at its primitive stage. The most realistic fear that has been discussed recently is that AI will take people's jobs.

Founded in 2012 by Rajesh Sawhney, GSF Accelerator has announced the list of startups graduating from its fifth batch. The graduating five startups have also received $65K to $100K as Seed funding, the startups are currently working out of Gurugram and are being mentored. The startups will travel to San Francisco and Tokyo next month with an aim to explore tech ecosystems outside of India. The accelerator provides promising startups with unparalleled access to venture and business networks, intensive mentoring, and initial capital. It is a multi-city programme and extends beyond India, with partners in London, Silicon Valley, and Singapore. Commenting on the development, Rajesh Sawhney, founder, GSF Accelerator said, "This is our fifth iteration and we have chosen 5 startups.

Today on In Case You Missed It: Ford used virtual reality to demo its "Autolivery" concept service at Mobile World Congress. The package delivery system of the future would consist of a self-driving van and a drone working together to deliver parcels and orders right to your door -- even if your door is on the 30th floor. While it's unlikely that anyone will see this system in action for several years -- the company anticipates the fleet won't be ready until at least 2021 -- it would go a long way to reducing urban gridlock and pollution. Meanwhile, Facebook is testing out an AI feature that uses pattern recognition to detect posts from users who may be in distress or suicidal. While users already have the ability to report a friend's status if they believe they're in trouble, the AI will flag status' for review by the Community Operations team. Additionally, there are new Messenger tools that help users connect to suicide prevention organizations in chat, and a report feature for potentially concerning livestreams.

Cobalt Robotics has launched their stylish security robot. The robot was designed by Yves Behar and as a fabric covered robot, it's putting a new spin on soft robotics! Behar's goal was to create a robot that didn't conform to Hollywood stereotypes but instead as an augmentation of human ability and an enhancement to the human environment. "Creating the right form for Cobalt is crucial to its success. As a service for security and concierge, it becomes part of an office culture. This balance between approachability and discretion became a thematic challenge throughout the design process. We decided that the robot should not adopt a humanoid personality. Instead, it should aesthetically align with the furniture and décor of the office environment. The Cobalt robot's semi-cylindrical self-driving mechanism, sensors and cameras are covered by a tensile fabric skirt. This helps maximize the access and usability of the internal technologies, creates airflow to prevent overheating, and conveys a soft and friendly persona." said Behar.

We discuss classification as the data-mining technique in patterns recognition and supervised machine-learning. Comparison of classification to other data exploration methods is presented and the importance of this approach is further discussed. We demonstrate the machine-learning method in finance and show how credit risk categorisation can be easily achieved with classification technique. Searching for patterns in economic and financial data has a long history. One of the oldest approached explored by econometricians was the data arrangement into groups based on their similarities and differences. As computer resources improved there was a growth in the scope and availability of new computational methods.

This is the competitive landscape for machine learning as of now. Countless posts have been written lately on who and what you need to follow in order to navigate this landscape, and rightly so. It's already enormous, and with any industry that's just awakening, the boundaries are not yet clear and everything is really up in the air. In short, if you were to want to build a chat application for your business or for fun, and looked at this chart, you would be confused on where to start. And no wonder: the latest buzzwords are AI and machine learning, startups all across the globe are getting in on the game, plenty who are just tacking the words on in hopes of quick funding – not without merit, as we've seen that investors are swarming in and some big exits have already been done.

When online businesses are investing large sums of money in hiring agents for customer support, Hutoma AI has introduced a virtual marketplace to hire deep learning chatbots from. Most importantly, nobody has to learn how to code as they can tutor this AI chatbot using previous chat examples and Hutoma's deep learning design. Hiring a dedicated human employee for answering numerous and unique queries from customers can be expensive; hence, a deep learning chatbot can drastically reduce expenses. These deep learning bots come with specialized knowledge for replying to common queries; moreover, you can always train it, without learning how to code for handling a complex conversation. The creators of this platform mainly wanted to construct an ecosystem for AI designers who will also be able to share and earn money from their deep learning chatbots.

While understanding machine learning has never been easier, it helps to know how to get started on some of the latest tools and features that are available. In this hands-on workshop with Galvanize, you'll learn the very basics of machine learning via the language of Python. Our community of staff, students, and professional instructors will guide you through the elementary aspects of SciKit Learn, a popular machine learning library for the Python language. We will learn a bit more about matrices, develop a linear model on a real data set, and play around in the sandbox if we have time. You'll leave this session equipped to write your own scripts and feel more prepared to take the next steps on your path to understanding machine learning.