In the past few years, the terms machine learning (ML) and deep learning have begun showing up frequently in many technology news and websites. The major difference between machine learning and other statistical methods, like data mining, is a popular subject of debate. In laymen's language, ML and data mining process use many of the same algorithms and techniques but one major difference lies in what the two methods predict. While data mining is used to uncover previously unknown patterns and knowledge, Machine learning is used to reproduce known patterns and knowledge. ML provides algorithms that resolve the problem based on the data, and the solution improves with time.

Explicit concept space models have proven efficacy for text representation in many natural language and text mining applications. The idea is to embed textual structures into a semantic space of concepts which captures the main topics of these structures. Despite their wide applicability, existing models have many shortcomings such as sparsity and being restricted to Wikipedia as the main knowledge source from which concepts are extracted. In this paper we highlight some of these limitations. We also describe Mined Semantic Analysis (MSA); a novel concept space model which employs unsupervised learning in order to uncover implicit relations between concepts. MSA leverages the discovered concept-concept associations to enrich the semantic representations. We evaluate MSA’s performance on benchmark data sets for measuring lexical semantic relatedness. Empirical results show superior performance of MSA compared to prior state-of-the-art methods.

February 9, 2016 -- Apcera, the leader in enterprise container management, today announced its presence at GigaOm AI, taking place February 15-16 in San Francisco. Mark Thiele, Apcera's chief strategy officer, will be moderating an industry panel titled, "Customer Experiences in AI," to be held on Thursday, February 16. The panel will also feature executives from Comcast, Cybric and Talla. Thiele is a globally recognized speaker and visionary on the topics of AI, cloud, IoT, data center, DevOps and IT leadership. Connect with Apcera at GigaOm AI To schedule one-on-one meetings with Apcera at the event, send email to press@apcera.com.

An Introduction to'Machine Learning' -- I came across this article and thought it was worth a share, the original article was surrounded in adverts and difficult to read, so I make no apologies for plagiarizing it! I have kept the original link at the bottom of the article, enjoy . . . The concept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence that keeps a computer's built-in algorithms current regardless of changes in the worldwide economy. If you would like to try your first Machine Learning Experiment -- take a look at this easy walkthrough https://t.co/JHwAShjRgj

Artificial Intelligence (AI) is often discussed in terms of the threat it poses, whether real (automation disrupting numerous industries) or imagined (AI taking over the human race). The AI for Good Foundation, however, is committed to helping the world through AI, and has partnered with Syngenta, an agrochemical company, to launch the Syngenta AI Challenge. Participants are required'to develop a model that could be used to help scientists analyse large amounts of seed data more efficiently and effectively'. They will be tasked to find out'which soybean varieties will perform better in farmers' fields in 2015 & 2016?'. To do this, entrants will be provided with a large range of data over a four-month period, before officially submitting their efforts by June 1 of this year.

The concept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence that keeps a computer's built-in algorithms current regardless of changes in the worldwide economy. Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources. The enormous amount of data, known as Big Data, is becoming easily available and accessible due to the progressive use of technology. Companies and governments realize the huge insights that can be gained from tapping into big data but lack the resources and time required to comb through its wealth of information.

Machine learning and artificial intelligence are all the rage today in venture capital circles. We've seen spectacular exits in the past few years, from Google absorbing Deepmind in 2014 for $500 million, to Twitter buying TellApart in 2015 for $533 million, and Intel swallowing Nervana in 2016 for $400 million. But these were all IT plays. Berkeley-based Lygos is engineering and designing microbes that convert low-cost sugar into high-value, specialty chemicals. Ultimately, the ability to design and optimize microbes, or program them, is becoming faster and cheaper than ever before.

An Introduction to'Machine Learning' -- I came across this article and thought it was worth a share, the original article was surrounded in adverts and difficult to read, so I make no apologies for plagiarizing it! I have kept the original link at the bottom of the article, enjoy . . . The concept that a computer program can learn and adapt to new data without human interference. Machine learning is a field of artificial intelligence that keeps a computer's built-in algorithms current regardless of changes in the worldwide economy. Various sectors of the economy are dealing with huge amounts of data available in different formats from disparate sources.

Elon Musk's plan for the future of energy will require almost half of the world's supply of Lithium; sustaining that may require the world's first autonomous trucking fleet to link the Tesla(TSLA) Gigafactory with Albermarle's(ALB) Chemetall-Foote Lithium Plant, the largest lithium producer in North America. Tesla's grand plan for an energy revolution is predicated on high capacity lithium-ion batteries, which power its consumer vehicles, power packs, and soon its autonomous semi trucks. The batteries, which are manufactured from lithium, will come from Tesla's Gigafactory. Tesla claims that in 2020 when it reaches full capacity, the Nevada facility will double the world production of Lithium ion batteries. Two hundred miles from the Gigafactory sits the Chemetall-Foote Lithium Plant, the largest lithium producer in North America.

Automated machine learning has become a topic of considerable interest over the past several months. A recent KDnuggets blog competition focused on this topic, and generated a handful of interesting ideas and projects. Of note, our readers were introduced to Auto-sklearn, an automated machine learning pipeline generator, via the competition, and learned more about the project in a follow-up interview with its developers. Prior to that competition, however, KDnuggets readers were introduced to TPOT, "your data science assistant," an open source Python tool that intelligently automates the entire machine learning process. For scikit-learn-compatible datasets, TPOT can automatically optimize a series of feature preprocessors and machine learning models that maximize the dataset's cross-validation accuracy, and outputs the optimal model as Python code leveraging scikit-learn.