Artificial intelligence will release humans to become more skilled at providing'soft services', according to the MD of a firm already using it. Phil Jones is the managing director at Manchester-based Brother UK, which provides hardware to businesses. He says many of the benefits of AI are already happening – but we don't shout about them enough. "AI just means more software doing jobs that humans used to do, but people don't always make the link," he says. "It has been talked about for over 50 years but is beginning to accelerate as a discussion point now, primarily because computational power has been consistently improving, as well as the amount of raw data available for the computers to crunch. "AI is really human-like intelligence, so it's things like where software algorithms are created to act more like humans.

The South African Jewish computer scientist and educator Seymour Papert, who died on July 31 at age 88, was a long-time fixture at the Massachusetts Institute of Technology. He pioneered artificial intelligence and co-invented the Logo programming language. Yet his work as a social reformer, rather than with machines per se, was a primordial obsession. The human rights activist Janet Levine's memoir "Inside Apartheid" describes how during her childhood in the early 1950s, the Papert family lived not far from her Johannesburg home. Their son Seymour, a university student, was "'in trouble' with the government for his student political activities. My father said that he did not know why someone as talented as Seymour would throw his life away'for the Schwartzes' (a derogatory Yiddish expression for black people)."

Artificial intelligence'will allow us to be more human' Air Liquide (EPA:AI) target price raised to 92EUR as disclosed today by Baader ...

Artificial intelligence may once have been the purview of science fiction books and movies, but increasingly, it is being used as a way for businesses to get a handle on the daily minutiae of transactions and customer interaction. One firm that has been marrying work processes and machine learning is WorkFusion, which, through AI cognitive services, seeks to automate what can be termed "knowledge work"-- in effect, everything from invoices to eCommerce itself -- in a process that has as its goal the elimination of repetitive tasks that can be prone to delays and human error. Machines, in this regard, take over at least some of the daily tasks that need to be performed on both small and grand scales. In recent news tied to the company, WorkFusion linked up with enterprise services firm Hexaware to provide automated business processes through digital managed services. In an interview with PYMNTS, Adam Devine, WorkFusion's vice president of product marketing and strategic partnerships, said that the process of workflow automation in its truest form can be thought of as intelligent augmentation -- an event "where technology makes people and work better."

Artificial intelligence has an unimaginable potential. Within the next couple of years, it will revolutionize every area of our life, including medicine. I am fully convinced that it will redesign healthcare completely – and for the better. Let's take a look at the promising solutions it offers. There are various thought leaders who believe that we are experiencing the Fourth Industrial Revolution, which is characterized by a range of new technologies that are fusing the physical, digital and biological worlds, impacting all disciplines, economies and industries, and even challenging ideas about what it means to be human.

Computers can be trained to be more accurate than pathologists in assessing slides of lung cancer tissues, according to a new study by researchers at the Stanford University School of Medicine. The researchers found that a machine-learning approach to identifying critical disease-related features accurately differentiated between two types of lung cancers and predicted patient survival times better than the standard approach of pathologists classifying tumors by grade and stage. "Pathology as it is practiced now is very subjective," said Michael Snyder, PhD, professor and chair of genetics. "Two highly skilled pathologists assessing the same slide will agree only about 60 percent of the time. This approach replaces this subjectivity with sophisticated, quantitative measurements that we feel are likely to improve patient outcomes."

Today, when Intel announced a new generation of Xeon Phi server chips, the emphasis was on their ability to handle A.I. Of all those servers, 7 percent were handling deep learning, while 95 percent were doing machine learning, she said. Of servers doing machine learning or deep learning, "the vast, vast majority of workloads are machine learning. They offer "advanced acceleration capabilities" for workloads like Google's TensorFlow deep learning framework, Google has said.

With the boom in AI affecting virtually every industry, there has been an explosion in the research and development of machine learning, a subfield of AI. And, perhaps, no company better illustrates what machine learning is capable of than Google's DeepMind. Founded in London in 2010 by Demis Hassabis, Shane Legg, and Mustafa Suleyman, DeepMind has developed machine learning systems that uses deep neural networks, reinforcement learning, and systems neuroscience-inspired models. The startup was purchased in January 2014 by Google for a rreported 400 million, with Hassabis remaining CEO of DeepMind. Instead of relying on explicit programming, DeepMind applies general-purpose learning algorithms to a large data set in order to "train" the system and make predictions.

The world of automation is growing quickly and I am thrilled to be a part of it. As you may already know, true robotic process automation (RPA) is rules-based, and involves training the bots to recognize structured data and respond accordingly within the workflow solution. Machine learning takes this to the next level, by working with unstructured data to provide solutions, within certain parameters, that it has been trained to recognize. The software analyzes data and learns from the ways in which humans complete a process or solve a problem. Over time, it develops the intelligence to understand and solve issues itself without having to be programmed.

This project looks into the application of Machine Learning (ML) techniques in the prediction of Immunogenicity (Categorical; Positive or Negative) based on a peptide and its associated amino acid properties. This study uses peptide data from the Immune Epitode Database (IEDB). The R package "Peptides" has been used to compute the amino acid properties and mashup with peptide data to enable the use of ML algorithms for immunogenicity analysis, particularly, the algorithms that are more efficient with numeric and categorical data instead of string sequence. Tensorflow is an open source software library ML that provides linear regression and classification algorithms (open sourced by Google in Nov 2015) for multi-dimensional arrays (aka "Tensors"). K-fold cross-validation as well as hold-out of test data was used to train and test the generated models.