If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Artificial Intelligence is transforming the landscape of human evolution, with one of the biggest landmarks for the technology being the declaration of an AI robot - Sophia - as a "national citizen" of the Kingdom of Saudi Arabia. Despite the fact that the earliest footprints of Artificial Intelligence can be traced back to 1956, its impact had been behind the curtains, until recently. During the last decade, AI has sparked polar reactions from observers, with some crowning it as the "face of the future", while others demarking it as the "beginning of the end". Elon Musk, the CEO of Tesla Motors, went as far as saying that AI would largely be attributable for the outburst of World War-III. On the other side of the fence, however, we have the likes of Mark Zuckerberg and Bill Gates, who could not have expressed more hope and faith in the revelation.
Since we started building bots more than 2 years ago, the landscape has seen massive interest and change. This makes it hard for companies and customers to figure out what's really happening and what they should do if they really want to build a chatbot for their business. Through this exercise, we deeply explored various bot platforms, bot use cases, and bot frameworks -- and we've arrived at some interesting observations and insights that may be useful to you (hopefully). Obviously, there's no way to squeeze everyone into the landscape, hence we selected those which fulfill these objectives: The global market for chatbots reached US$88.3 million in 2016. The market will grow to 36% CAGR, to more than $1 billion, through 2023.
The telephone remains as the main platform used by consumers to connect with companies they have business with. In fact, a recent research conducted by Forrester revealed that the phone is still the most widely-used customer service channel – with 73% of customers calling into the call center. In a separate study from Arizona State University, the prominence of the telephone was also indicated: Customers are 11 times more driven to use a telephone to complain when they are angry. However, in today's high-touch constantly connected world where information spreads rapidly, the term using a call center in customer service may seem like a thing of the past. These days, a lot of organizations and companies are looking to the next big things in the customer service technology and communication channels – such as the Artificial Intelligence and Bots.
Feature selection is a very important technique in machine learning.We need to be able to solve it to produce models. It's not an easy technique though. Feature Selection requires heuristic processes to find an optimal machine learning subset. In the previous post we discussed the brute force algorithm as well as forward selection and backward elimination which were both not a great fit. What other options are there?
Speckling the surface of one of Mars' oldest impact basins, NASA's Mars Reconnaissance Orbiter has spotted a sprawling expanse of'honeycomb' landforms, with individual cells of up to 6 miles wide. The origin of these textured features has long remained a mystery, as scientists debate which type of natural process could be responsible, from glacial events to wind erosion. It's possible that multiple processes are at play, according to NASA, with evidence suggesting the honeycombs and the surrounding landscape in Mars northwestern Hellas Planitia may still be undergoing activity today. Speckling the surface of one of Mars' oldest impact basins, NASA's Mars Reconnaissance Orbiter has spotted a sprawling expanse of'honeycomb' landforms, with individual cells of up to 6 miles wide. According to NASA, the area has features of different natural processes, suggesting activity may still be reshaping the land today.
The Second Payments Services Directive (PSD2) is a wide-ranging piece of European legislation that will transform the payments industry. Alternatively referred to as the'Open Banking' initiative, PSD2 promises to increase consumer choice in the payments sector. It will force banks, who have traditionally held a monopoly over the provision of payments services, to allow new, third party providers to'build on top of' their existing payments infrastructure and offer their own services. The directive, incoming in January, is of seismic importance to both banks and payments providers. Banks are sensing the need to move quickly so as not to simply become'utilities' similar to the water or telephone company, with only a small stable of products to offer.
A team of researchers from the University of Lyon, Purdue and Ubisoft have published a paper showing what may well be the future of creating video game worlds: an AI that is able to construct most of its own 3D landscapes. Similar to Nvidia's work that is able to conjure its own celebrity mugshots, the tech would require only minimal input from a human, who would just have to contribute some basic requirements, draw some lines then let the AI do all the hard work: namely, filling in all the gaps with elevation, ridges and natural-looking rock formations. As the paper states, this kind of tech would only be the beginning; future research could lead to landscapes being generated entirely by the AI, and for the model to be able to handle more complex environmental features like sand and vegetation.
How can companies identify--and source--the technologies that will be critical for crafting a strategy to keep up in the shifting automotive landscape? The automotive industry is in the early phases of what is expected to be rapid and fundamental change. Technology is the key to further penetration of all these trends, as well as the developing business models that allow companies to capitalize on them. The industry players--traditional automotive companies and new entrants alike--that identify and secure those technological resources will be best positioned to benefit in the new mobility landscape. Thus, industry players need to think about sourcing underlying technologies rather than acquiring single products or services.
There is a consensus that feature engineering often has a bigger impact on the quality of a model than the model type or its parameters. Feature selection is a key part of feature engineering, not to mention Kernel functions and hidden layers are performing implicit feature space transformations. Therefore, is feature selection then still relevant in the age of support vector machines (SVMs) and Deep Learning? First, we can fool even the most complex model types. If we provide enough noise to overshadow the true patterns, it will be hard to find them.
Deployment is a big chunk of using any technology, and tools to make deployment easier have always been an area of innovation in computing. For instance, the difficulties and uncertainties of installing software and keeping it up-to-date were one factor driving companies to offer software as a service over the Web. Likewise, big data projects present their own set of issues: how do you prepare and ingest the data? How do you view the choices made by algorithms that are complex and dynamic? Can you use hardware acceleration (such as GPUs) to speed analytics, which may need to operate on streaming, real-time data?