Regressions are widely used to estimate relations between variables or predict future values for a certain dataset. If you want to know how much of variable "x" interferes with variable "y" you might want to do a regression in your data. If you have a bunch of data points in time, and you want to know what is your data going to look like in the future, you also might want to do regression. I will try to describe the steps that helped me successfully build linear and non-linear regression in R, using polynomials and splines. I am not going to go on too much details on each method.

Machine learning and other variations of artificial intelligence (AI) are expected to proliferate in the enterprise in 2017. The majority of IT players, including today's leading technology companies, have invested in the space and plan to increase efforts for the foreseeable future, according to analysts who cover the market. However, while machine learning has generated tremendous interest throughout the enterprise, a wide gap still exists between research and beneficial use cases in the real world. And only a small number of companies have the resources to actually drive AI innovation and deliver it to the masses, sources say. During last month's Code Enterprise conference, LinkedIn CEO Jeff Weiner said AI was one of leading factors in the company's decision to be acquired by Microsoft.

I was reminded of a mathematical hypothesis called the singularity when I read Vinod Khosla's recent interview in the Wall Street Journal and his prediction of massive job displacement and the growth of new industries due to the widespread adoption of artificial intelligence (AI). The singularity is a point and phase in the future when bio, nano, energy, robotic, and computer technology will develop at such a rate, become so advanced, and have such a profound impact on humanity, that today's society has no means to understand or describe what life will be like at that time in the future. It made me wonder how far and fast we are heading in the same explosion of unfathomable change occurring today in information security. Just as IT revolutionized all forms of business in the last half-century, and the Internet in turn revolutionized IT in the last quarter-century, the trajectory we are on now places AI squarely at the next technology inflection point. The study of history often provides a strong predictor of human societal change.

Machine Learning is a vast area of Computer Science that is concerned with designing algorithms which form good models of the world around us (the data coming from the world around us). Within Machine Learning many tasks are - or can be reformulated as - classification tasks. In classification tasks we are trying to produce a model which can give the correlation between the input data and the class each input belongs to. This model is formed with the feature-values of the input-data. For example, the dataset contains datapoints belonging to the classes Apples, Pears and Oranges and based on the features of the datapoints (weight, color, size etc) we are trying to predict the class. We need some amount of training data to train the Classifier, i.e. form a correct model of the data.

Our smartphone currently represents the most expensive area to be purchased per squared centimeter (even more expensive than the square meters price of houses in Beverly Hills), and it is not hard to envision that having a bot as unique interfaces will make this area worth almost zero. None of these would be possible though without heavily investing in speech recognition research. Deep Reinforcement Learning (DFL) has been the boss in town for the past few years and it has been fed by human feedbacks. However, I personally believe that soon we will move toward a B2B (bot-to-bot) training for a very simple reason: the reward structure. Humans spend time training their bots if they are enough compensated for their effort.

Artificial intelligence (AI) had a coming out party of sorts in 2016. Even though it has been in development for decades, this year, with the perfect combination of cheap computing power and access to increasing amounts of data, it seems AI's time has come. Its first foray in business has been directed at making salespeople more efficient at every level of the sales workflow. If you think about it, it makes sense to start with the part of the company that drives revenue. Certainly the vendors recognize that, says Alan Lepofsky, an analyst at Constellation Research, who is working on the impact of AI on work.

Computers powered by artificial intelligence are smart enough to threaten a range of jobs, whether computers developing treatment plans for cancer patients or Amazon.com That impact is already being felt. The World Economic Forum expects automation, including AI, to result in the loss of at least 5 million jobs globally by 2020. In the view of Genpact (G) CEO NV "Tiger" Tyagarajan, however, the bigger question is how many jobs such technology will ultimately create. Amazon is opening a new brick-and-mortar store, but without a checkout line.

Becoming more affordable and easier to program, they are widely present in our everyday life. You name the field, the robots are there: agriculture, tourism, e-commerce, medicine… They even help with household chores. With the accelerating speed of robotics development, it appears a very tempting field for investment. Last month, Credit Suisse's Global Equity Research team published a report examining its potential. We have already accepted big robots working in agriculture (e.g. Some of them, such as smartphones or sensors in cars, have already become an integral part of our lives; others, such as robots with tactile sensing, are still on the way.

Summary: Watson is a remarkably flexible and complete AI development platform. To understand how you might build new services for your current employer or imagine your own Watson-based startup, look at these 30 companies that are leading the way. In our recent reviews of historical Watson and the modern Watson of today we concluded that IBM's Watson Group may have the first or at least the current strongest comprehensive AI platform. This is the first time that we know of that all three elements of AI have been brought together in a single user friendly platform: image processing, text and speech processing, and knowledge retrieval. This is not so much a platform for data scientist to use to expand the capabilities of AI as it is a platform for business users (with the aid of data scientists) to exploit the capabilities of modern AI by building new products and services.

A visitor speaks to Baidu's robot Xiaodu at the 2015 Baidu World Conference in Beijing, China, September 8, 2015. How important is artificial intelligence (AI) to Baidu? Gone is the era of PC, and soon will we say goodbye to the era of mobile internet ... We believe that coming is the era of artificial intelligence. Andrew Ng, Baidu's chief scientist, has some experience in this area. During his previous tenure at Google parent Alphabet, he led the Google Brain AI project.