You only look once (YOLO) is a state-of-the-art, real-time object detection system. On a Titan X it processes images at 40-90 FPS and has a mAP on VOC 2007 of 78.6% and a mAP of 44.0% on COCO test-dev. Prior detection systems repurpose classifiers or localizers to perform detection. They apply the model to an image at multiple locations and scales. High scoring regions of the image are considered detections.

AI's strongest applications are data-hungry. Pioneers in the field, such as Facebook, Google, and Uber, have each secured a "privileged zone" by gaining access to current and future data, the raw material of AI, from their users and others in ways that go far beyond traditional data harvesting. Their scale gives them the ability to run more training data through their algorithms and thus improve performance. In the race to leverage fully functional self-driving cars, for example, Uber has the advantage of collecting 100 million miles of fleet data daily from its drivers. This data will eventually inform the company's mobility services.

MEPs have called for the adoption of comprehensive rules for how humans will interact with artificial intelligence and robots. The report makes it clear that it believes the world is on the cusp of a "new industrial" robot revolution. It looks at whether to give robots legal status as "electronic persons". Designers should make sure any robots have a kill switch, which would allow functions to be shut down if necessary, the report recommends. Meanwhile users should be able to use robots "without risk or fear of physical or psychological harm", it states.

Hot topics like "big data", "machine learning", "data science" are now dominating in the scientific community. In the past 10 years alone, data availability has increased exponentially (and not even in a squared, or cubed sort of way… we are talking on the order of 1010 if not more). Exabytes (1018 or one QUINTILLION bytes!!?) of information are being passed, stored, saved and analyzed on a monthly (perhaps weekly?) basis. This includes credit card transactions (in November 2015, there were approximately 242 million credit card transactions in the United Kingdom alone;(source: BBA)), web searches (Just think about how many times you use Google in the run of a day, and interpolate that out to the 40% of the world who have access to the internet), and any time a user (you) clicks on a link you found on Facebook. When you combine this with the countless other data coming in, it is nearly overwhelming to think about.

Linear Regression is about fitting a straight line from the scatter plot,key challenge here what constitutes a best fit line in other words what would be best values of and . The general idea is to find a line ( its coefficients) such that total error is at the minimum. There is a standard explanation that we need to minimize the total square error, which means we have to solve a minimization problem to solve optimal values of the coefficients. Obviously this method involves quite a lot of mathematics or calculus etc. which would not provide any institution or illustration, instead we will use a little of vector algebra and associated geometry to build the intuition about the solution.

If manufacturers want to sustain and grow their customer bases in a competitive environment, their products need to fulfill increasingly high quality and reliability standards. Automakers, for example, now have a target defect rate for the integrated systems of less than 1 percent. That's putting pressure on the original equipment makers (OEMs) and their suppliers who have to meet these targets at the same time that products and manufacturing processes are becoming increasingly complex and featuring numerous activities that impact quality, performance, and yield. To prevent failures of components, systems, and ultimately the product, these manufacturers need reliable methods to find defects. But quality control today is, in many cases, still performed by human inspectors, which limits its reliability and efficiency.

Deals to AI startups reached a record high last year, from 160 deals in 2012 to 658 in 2016. Although non-US deal share has been increasing over the time period, well over a majority of deals, around 70%, went to startups in the US in the last 5 years. The top 3 states for deals were California (51%), New York (11%), and Massachusetts (9%). Using the CB Insights database, we mapped the most well-funded AI startups in each of the 35 states where there have been equity deals to an AI company in the last five years. The map only includes companies that have not exited and only includes equity financing.

Artificial intelligence is one of the most hot-button topics these days. Countless industries are beginning to utilize AI tools in the interest of becoming more agile and responding to market demands more quickly and efficiently – and the finance realm is no different. In fact, AI is becoming a major buzzword in the financial services industry. Banks are beginning to utilize these kinds of technologies to make sense of all the customer data flowing into their organizations, which helps to capitalize on key insights and create better business practices by addressing industry concerns as they come up. It's becoming clear that banks will need to create even stronger AI strategies in the near future in order to deal with the rapidly changing regulatory environment and customer demand.

What if artificial intelligence actually made a difference in our everyday lives? If you think about it, the technology for processing information more like a human is still in an early stage. It shows up in chatbots and on speakers like the Amazon Echo. Yet, many of the services we use each day are still not AI-enabled, which is unfortunate. It's one thing to make a car we can't afford or a speaker smarter, but how about these common products and services?

When Everest Group asked me to lead its research on Service Delivery Automation (SDA) last year, the significance of this new role was not immediately clear to me. It is only now, a year older and wiser, that I realize that new roles and job titles are harbingers of change. They are one of the many effects of disruptive technology and its impact on the workplace. What are the implications and why is the emergence of such new titles significant? New titles are popping up all over the industry.