Artificial Intelligence (AI) and Machine Learning (ML) are poised to help companies make dramatic shifts in performance, shareholder value and business development over the next two years. AI opens the door to analyze massive amounts of data and deliver critical insights that organizations across a wide variety of industries can use to improve processes, drive profitability, and increase their competitive advantage. To better understand how this game-changing technology is being applied to drive business success today and where it is headed in the near future, Protiviti and ESI ThoughtLab interviewed more than 300 executives around the globe, reaching across functions, company size and industry, including healthcare, technology, financial services and consumer products. Results from this important research are included in this report, including how businesses are investing in AI, where the benefits of AI/ML are being seen now and will be seen in the immediate future, challenges and perceptions regarding AI talent, and obstacles that executive leadership will face. The clear picture gleaned from the research is this: Companies who are leading the charge with advanced AI are finding that it is a real game changer, while companies who are still lagging behind will soon experience a competitive disadvantage.

In previous articles, I've looked at a variety of video games that have proven useful test-beds for AI research, with the likes of Ms. Pac-Man, Super Mario Bros. and more recently StarCraft. But in this instance I want to look at a genre that is still relatively new whilst presenting exciting opportunities for AI research: Multiplayer Online Battle Arena's (MOBA). The MOBA genre is undoubtedly one of the most popular in gaming today, but what impact could this have upon AI research? I'm going to provide an overview of MOBA's as a genre, what aspects of their design can prove interesting to AI research and look at some projects that are now bearing fruit both in academia and in corporate research labs. Multiplayer Online Battle Arena's are an offshoot of Real-time Strategy (RTS) games, originating with the Aeon of Strife map for Blizzards StarCraft, followed by the'Defence of the Ancients' mod for WarCraft III: Reign of Chaos and its expansion The Frozen Throne.

Machine learning is an ever-evolving field, so it can be easy to feel like you're out of the loop on the latest developments changing the world this week. One of those emerging areas that have been getting a lot of buzz lately is GANs--or generative adversarial networks. So to keep you in the machine learning loop, we've put together a short crash course on GANs: With generative models, the aim is to model the distribution of a given dataset. For the generative models that we're talking about today, that dataset is usually a set of images, but it could also be other kinds of data, like audio samples or time-series data. There are two ways to go about getting a model of this distribution: implicitly or explicitly.

As if there weren't enough security challenges, a forecast for the coming year warns the brave new world of AI and machine learning brings with it a whole new set of threats as microservices, the Internet of Things and multi-cloud deployments expand attack surfaces for hackers constantly probing for weaknesses. Market analyst Gartner's list of key technology trends for 2020 includes a compelling list of emerging trends, ranging from "hyper-automation" to the "democratization of expertise." Still, these emerging capabilities come with a price: As AI and machine learning add another layer of technological complexity, "Security generalists cannot address the wide spectrum of risks efficiently in the current risk landscape," Gartner has warned. The connection of platforms, edge devices and users expecting instant access to data--structured and unstructured--has raised the stakes for security as AI and machine learning enter the enterprise mainstream. "Security and risk leaders should focus on three key areas -- protecting AI-powered systems, leveraging AI to enhance security defense and anticipating nefarious use of AI by attackers," the market analyst warned in its rankings of AI-centric emerging technologies.

With the industry 4.0 revolution around, Research N Reports presents a detailed analysis of Cognitive Computing market that offers latest insights for business professionals. Using BI tools such as Factiva and Hoover, the report offers a comprehensive analysis and is a mix of market intelligence studies and industry insights. Prepared by a panel of highly experienced market analysts and consultants, the report is spread across 137 pages offering chapter wise detailed market analysis that enables the clients with multiple data points and encourages them to have a 360 degree overview of the market performance. Clients can ask for sample of this report that gives a detailed overview of the market conditions, driving and restraining factors, segments, trends and opportunities. Covering the latest information about the market, the samples can give a basic understanding upon the report contents and its format.

In this presentation, Kathrin Melcher, who works as a data scientist at KNIME, will give an overview of KNIME Software, including the open-source tool KNIME Analytics Platform for creating data science applications and services and also the different deployment options you have when using KNIME Server. While the structure is often similar--data collection, data transformation, model training, deployment--each project required its own special trick, whether this was a change in perspective or a particular technique to deal with the special case and business questions. You'll learn about demand prediction in energy, anomaly detection in IoT, risk assessment in finance, the most common applications in customer intelligence, social media analysis, topic detection, sentiment analysis, fraud detection, bots, recommendation engines, and more. Join us to learn what's possible in data science. She holds a Master's Degree in Mathematics from the University of Konstanz, Germany.

Each video is created with real time scenario examples in simple language. So that anyone without programming knowledge can understand in depth about Artificial Intelligence and Machine Learning. The contents were prepared based on maximum queries searched in google or posted in AI forum. At the end of this course you will get clear clarity on how much effort needed to start your career in Artificial Intelligence or Machine Learning Projects. For Non-English speaking students, I enabled the Auto Caption now.

He and his team have played a key part in weaving ML into the very fabric of Workday's underlying platform, which is critical to delivering compelling experiences and outcomes without customers even needing to realize it is there. Earlier in his career, while at a number of Silicon Valley companies, he played a part in making the technology we rely on everyday--GPS, and wifi, for example--so ubiquitous that most of us take these revolutionary technologies for granted. Chakraborty also co-founded and served as chief operating officer at GridCraft, a company that developed simple-to-use data analytics tools that Workday acquired in 2015. Now, as senior vice president of tools and technology at Workday, Chakraborty is responsible for the infrastructure on which our applications are built. In particular, he's leading the charge to make sure that machine learning helps customers make faster, better decisions using all of Workday's products.

Called LOGAN, the deep neural network, i.e., a machine of sorts, can learn to transform the shapes of two different objects, for example, a chair and a table, in a natural way, without seeing any paired transforms between the shapes. All the machine had seen was a bunch of tables and a bunch of chairs, and it could automatically translate shapes between the two unpaired domains. LOGAN can also automatically perform both content and style transfers between two different types of shapes without any changes to its network architecture. The team of researchers behind LOGAN, from Simon Fraser University, Shenzhen University, and Tel Aviv University, are set to present their work at ACM SIGGRAPH Asia held Nov. 17 to 20 in Brisbane, Australia. SIGGRAPH Asia, now in its 12th year, attracts the most respected technical and creative people from around the world in computer graphics, animation, interactivity, gaming, and emerging technologies. "Shape transform is one of the most fundamental and frequently encountered problems in computer graphics and geometric modeling," says senior coauthor of the work, Hao (Richard) Zhang, professor of computing science at Simon Fraser University.

Data science is labor-intensive and human experts are scarce but heavily involved in every aspect of it. This makes data science time consuming and restricted to experts with the resulting quality heavily dependent on their experience and skills. To make data science more accessible and scalable, we need its democratization. Automated Data Science (AutoDS) is aimed towards that goal and is emerging as an important research and business topic. We introduce and define the AutoDS challenge, followed by a proposal of a general AutoDS framework that covers existing approaches but also provides guidance for the development of new methods. We categorize and review the existing literature from multiple aspects of the problem setup and employed techniques. Then we provide several views on how AI could succeed in automating end-to-end AutoDS. We hope this survey can serve as insightful guideline for the AutoDS field and provide inspiration for future research.