This article presents a new result about the size of a multilayer neural network computing real outputs for exact learning of a finite set of real samples. The architecture of the network is feedforward, with one hidden layer and several outputs. Starting from a fixed training set, we consider the network as a function of its weights. We derive, for a wide family of transfer functions, a lower and an upper bound on the number of hidden units for exact learning, given the size of the dataset and the dimensions of the input and output spaces.

When we hear about Artificial Intelligence (AI), the very first thought comes into our mind as it's being our personal home, office, or driving assistant. Because of the existing media representation on AI and the current progress of AI in the technology market, it is undoubtedly obvious to have such expectations from it. The involvement of AI in the technology space has been driven to a certain extent since the proliferation, especially in data analytics, and in which market data analytics to be precise. Many market researchers and data analysts believe that AI is an essential factor driving better performance efficiency and customer satisfaction, which eventually helps companies get better sales and revenues. According to one market survey, around 93% of market researchers consider AI as an industry opportunity, and 80% agree on AI driving a positive impact on the market.

To address the concern for the ethical and societal implications of artificial intelligence systems, a possible solution is to have AI systems be audited for harm by investigators. We at the Frankfurt Big Data Lab at the Goethe University of Frankfurt, together with a team of international experts defined a novel holistic and analytic processes to assess Ethical AI, called Z-Inspection. Z-Inspection is a general inspection process for Ethical AI which can be applied to a variety of domains such as business, healthcare, public sector, etc. To the best of our knowledge, Z-Inspection is the first process that combines a holistic and analytic approach to assess Ethical AI in practice. Our assessment takes into account the "Framework for Trustworthy AI" and the seven key requirements that AI systems should meet in order to be deemed trustworthy, defined by the independent High-Level Expert Group of Artificial Intelligence [1], set by the European Commission, and also confirmed by a recent report of The Organization for Economic Co-operation and Development (OECD).

For almost a century, movies and literature have explored the possibility of technology beginning to think for itself--often with a dystopian spin. Consider that today, artificial intelligence is known far and wide as AI--which also happens to be the title of the 2001 Steven Spielberg drama (based on a 1969 short story) where indeed, machines inherit the world from their human creators. And just months ago, at least two billionaire visionaries, Tesla Founder Elon Musk and Dallas Mavericks owner Mark Cuban, spoke out about the potential evils. Musk has called AI "our biggest existential threat" while Cuban, for his own part, used much stronger language to express his anxiety. Fears of the future are one thing; the present reality, however, represents quite another.

Accenture has acquired OPS Rules, a boutique analytics consulting company that specializes in the application of data science to create supply chain and operations analytics solutions. When the acquisition is completed, Accenture will add new operations analytics professionals to its team to apply machine learning and optimization techniques to develop analytics approaches for clients across many industries. Founded in 2012, OPS Rules has offices in Waltham, Mass., and Richardson, Texas. OPS Rules is led by David Simchi-Levi, a professor of engineering systems at the Massachusetts Institute of Technology (MIT) and renowned supply chain and operations analytics expert. Simchi-Levi and his team will join Accenture Analytics, part of Accenture Digital, and will also be a part of Accenture's Data Science Center of Excellence, an innovation team that focuses on solving immediate and complex client problems through advanced analytics approaches, including machine learning, deep learning, text analytics and more.

IT services provider Accenture announced its deal to acquire OPS Rules, an analytics consulting company based in the US. The acquisition of OPS Rules enables Accenture to expand its machine learning and operations analytics capabilities. Last year, Accenture also acquired Gapso, an analytics services and solutions provider in Brazil that assists enterprises to solve supply chain and logistics challenges. OPS Rules, founded in 2012, specializes in the application of data science to create supply chain and operations analytics solutions. The US head-quartered Accenture aims to add new operations analytics professionals to its team that apply machine learning and optimization techniques to develop innovative analytics approaches for clients.