John C. Mauro is Professor of Materials Science and Engineering at the Pennsylvania State University. John earned a B.S. in Glass Engineering Science (2001), B.A. in Computer Science (2001), and Ph.D. in Glass Science (2006), all from Alfred University. He joined Corning Incorporated in 1999 and served in multiple roles there, including Senior Research Manager of the Glass Research department. Mauro is the inventor or co-inventor of several new glass compositions for Corning, including Corning Gorilla Glass products. Mauro joined the faculty at Penn State in 2017 and is currently a world-recognized expert in fundamental and applied glass science, statistical mechanics, computational and condensed matter physics, thermodynamics, and the topology of disordered networks.

In the next three years, as many as 120 million workers in the world's 12 largest economies may need to be retrained or reskilled as a result of AI and intelligent automation, according to a new IBM Institute for Business Value (IBV) study. In addition, only 41 percent of CEOs surveyed say that they have the people, skills and resources required to execute their business strategies. The study, which includes input from more than 5,670 global executives in 48 countries, points to compounding challenges that require a fundamental shift in how companies meet and manage changing workforce needs throughout all levels of the enterprise. According to the global research, the time it takes to close a skills gap through training has increased by more than 10 times in just four years. In 2014, it took three days on average to close a capability gap through training in the enterprise; in 2018, it took 36 days.

As if manufacturers didn't already have enough on their hands trying to find suitable applicants for their shop floors and R&D departments, the world of artificial intelligence is about to explode onto the scene. And when it does, the scramble for talent will only grow maddeningly tougher. This may sound like trouble, but there's a tremendous upside. According to a newly released study by the MAPI Foundation and the Information Technology and Innovation Foundation (ITIF), not only will AI enable machines to do a lot more--but it will also empower humans to do a lot more as well. That means an upsurge of new kinds of jobs related to developing new AI solutions, leading new AI business strategies and supervising AI implementations.

Machine Learning is a field of computer science concerned with developing systems that can learn from data. Like statistics and linear algebra, probability is another foundational field that supports machine learning. Probability is a field of mathematics concerned with quantifying uncertainty. Many aspects of machine learning are uncertain, including, most critically, observations from the problem domain and the relationships learned by models from that data. As such, some understanding of probability and tools and methods used in the field are required by a machine learning practitioner to be effective.

The following is a guest post by Pete McCain, a technology startup enthusiast associated with App Velocity. If you would like to submit a guest post, please contact us. There was a time when we all were highly skeptical about online education because we couldn't fathom a computer screen replacing our classrooms and the education ideals that come with them. But now examining the impact of online education, we can clearly see how eagerly we've embraced the idea of e-learning. It has levelled up education in the developed parts of the world and democratized education where schools and teachers couldn't reach.

These models tend to be less parsimonious and have a lot of configuration overhead and sensitivity to data provenance

This article is part of Demystifying AI, a series of posts that (try to) disambiguate the jargon and myths surrounding AI. Last week, the Allen Institute for Artificial Intelligence (AI2) introduced Aristo, an artificial intelligence model that scored above 90 percent on an 8th grade science test and 80 percent on a 12th-grade exam. Passing a science test might sound mundane, if you're not familiar with how deep learning algorithms, the current bleeding edge of AI, work. After all, AI is already performing tasks such as diagnosing cancer, detecting fraud and playing complicated games, which are much more complicated than answering simple science questions about the moon and squirrel populations. But despite its fascinating achievements, deep learning struggles when it comes to tackling problems that require reasoning and commonsense.

If you've ever attempted to learn Thai, you can assume that this Southeast Asian language is extremely difficult -- if not the most difficult -- for machines to also understand. Thai is a character-based language with numerous quirks that disrupt natural language processing algorithms. Because of these quirks, leading NLP engines fail to understand Thai beyond the surface-level, causing an underwhelming customer experience. First, the language consists of several types of interjection words in a single sentence. Many of these words do not carry any meaning relevant to the sentence's intent; these words are most often used to indicate emotion or an expression of politeness.

Who are the primary implementers of machine learning and data science today? A new market research report shows that large enterprises and smaller businesses are the first movers. That's because big companies have the money to invest and smaller ones are unencumbered by long chains of command. Mid-sized enterprises are having a harder time. Without the resources of the bigger players or the agility of the little players, they are slower to implement data science and machine learning. But if they take a smart approach to their efforts, they can get significant value out of where they do invest.

Python has become the go-to programming language for developers all around the world. From tech giants to consumer-based companies, almost every organization is leveraging the power of Python as it is a general-purpose high-level programming language. With rising complex data sets, the need for efficient algorithms has also increased. Technologies like machine learning are honing the capabilities of Python to create efficient models that simplify complex data sets. If you like working with data sets and have the capability to handle challenging tasks in an organization, consider machine learning using a python course. There are many online courses available for Python that would help you get a step closer to machine learning.