AI is set to have a big impact on high-wage, white-collar, and tech jobs, according to a new Brookings Institution study released today. The report analyzes overlap between job descriptions and patent database text, using NLP to assign each job an exposure score. "High-tech digital services such as software publishing and computer system design -- that before had low automation susceptibility -- exhibit quite high exposure, as AI tools and applications pervade the technology sector," the report reads. The AI exposure score was created by researcher Michael Webb to predict the likelihood AI will affect certain cities, regions, occupations, industries, or demographic groups, but is not designed to determine whether that impact is positive or negative. Exposure to AI could mean that the tech will likely augment or change how certain occupations work, or it could mean a higher likelihood AI will take your job.

Light behaves differently in water than it does on the surface -- and that behavior creates the blur or green tint common in underwater photographs as well as the haze that blocks out vital details. But thanks to research from an oceanographer and engineer and a new artificial intelligence program called Sea-Thru, that haze and those occluded colors could soon disappear. Besides putting a downer on the photos from that snorkeling trip, the inability to get an accurately colored photo underwater hinders scientific research at a time when concern for coral and ocean health is growing. That's why oceanographer and engineer Derya Akkaynak, along with Tali Treibitz and the University of Haifa, devoted their research to developing an artificial intelligence that can create scientifically accurate colors while removing the haze in underwater photos. As Akkaynak points out in her research, imaging A.I. has exploded in recent years.

IBM believes 100% of jobs will eventually change due to artificial intelligence, and new empirical research released last October 30 from the MIT-IBM Watson AI Lab reveals how. The research, The Future of Work: How New Technologies Are Transforming Tasks, used advanced machine learning techniques to analyze 170 million online job postings in the United States between 2010 and 2017. It shows, in the early stages of AI adoption, how tasks of individual jobs are transforming and the impact on employment and wages. "As new technologies continue to scale within businesses and across industries, it is our responsibility as innovators to understand not only the business process implications, but also the societal impact," said Martin Fleming, vice president and chief economist of IBM. "To that end, this empirical research from the MIT-IBM Watson AI Lab sheds new light on how tasks are reorganizing between people and machines as a result of AI and new technologies."

A new international study commissioned by WP Engine and conducted by researchers at The University of London and Vanson Bourne explored the present and near future of artificial intelligence (AI)-driven human digital experiences on the web, and the often tenuous but also potentially rewarding relationship between consumers, brands and AI. The study, which surveyed consumers and enterprise companies (1,000 employees or more) in the US, UK and Australia, found that in an era of purpose-driven consumption, values--such as transparency, trust and humanness--are key drivers that unlock value in AI. According to IDC, worldwide spending on artificial intelligence (AI) systems is forecast to reach $35.8 billion in 2019, an increase of 44% over the amount spent in 2018. Much of that growth will come from the application of AI online because there is a natural, evolutionary symbiosis between AI and the internet. However, it was a sudden burst of activity starting in 2013 that marks the beginning of what we might term the modern AI period, especially for digital and digital experiences, characterised predominantly by automated content creation, programmatic ad buying in 2014, and intelligent search.

A new platform that measures the body's immune-protein response, coupled with machine learning, can accurately distinguish between bacterial and viral infections within minutes – an effective tool in the fight against AMR. When a patient presents with fever, in many cases, the question comes down to whether it is a bacterial or viral infection, and if to treat, or not to treat, with antibiotics. Making this diagnosis can be challenging as bacterial and viral infections are frequently clinically indistinguishable. As a result, the disease causing pathogen is not clearly identified in as many as two out of three patients with acute infection, even when applying cutting edge microbiological tools.1–3 A complementary diagnostic paradigm has emerged in recent years that overcomes the limitations of direct pathogen detection, namely harnessing the body's immune-response to infection.

Sainsbury's commercial and technology teams are working with Accenture to implement machine learning processes that they say are providing the retailer with better insight into consumer behaviour. Using the Google Cloud Platform (GCP), the key aim of the collaboration is to generate new insights on what consumers want and the trends driving their eating habits. By tapping into data from multiple structured and unstructured sources, the supermarket chain has developed predictive analytics models that it uses to adjust inventory based on the trends it spots. According to Alan Coad, managing director of Google Cloud in the UK and Ireland, the platform can "ingest, clean and classify that data", while a custom-built front-end interface for staff can be used "to seamlessly navigate through a variety of filters and categories" to generate the relevant insights. Phil Jordan, group CIO of Sainsbury's, said: "The grocery market continues to change rapidly. "We know our customers want high quality at great value and that finding innovative and distinctive products is increasingly important to them.

Sentiment essentially relates to feelings; attitudes, emotions and opinions. Sentiment Analysis refers to the practice of applying Natural Language Processing and Text Analysis techniques to identify and extract subjective information from a piece of text. A person's opinion or feelings are for the most part subjective and not facts. Which means to accurately analyze an individual's opinion or mood from a piece of text can be extremely difficult. With Sentiment Analysis from a text analytics point of view, we are essentially looking to get an understanding of the attitude of a writer with respect to a topic in a piece of text and its polarity; whether it's positive, negative or neutral.

Artificial Intelligence (AI) is a hotly debated topic, particularly in the context of its impact on the labour market and the workforce. These vital discussions are all too often based on assumptions and desktop projections rather than on concrete, objective data. New research from LinkedIn's Economic Graph uncovers novel, evidence-based insights into the state of AI talent development in the European Union (EU) labour market, and identifies emerging trends that can help inform policymaking in this area. The European Commission has clear ambitions and goals for AI, but right now Europe is lagging behind its peers in developing talent. The U.S. employs twice as many AI-skilled individuals than the EU, despite the American total labour force being just half the size.

For developers, advances in hardware and software for machine learning (ML) promise to bring these sophisticated methods to Internet of Things (IoT) edge devices. As this field of research evolves, however, developers can easily find themselves immersed in the deep theory behind these techniques instead of focusing on currently available solutions to help them get an ML-based design to market. To help designers get moving more quickly, this article briefly reviews the objectives and capabilities of ML, the ML development cycle, and the architecture of a basic fully connected neural network and a convolutional neural network (CNN). It then discusses the frameworks, libraries, and drivers that are enabling mainstream ML applications. It concludes by showing how general purpose processors and FPGAs can serve as the hardware platform for implementing machine learning algorithms. A subset of artificial intelligence (AI), ML encompasses a wide range of methods and algorithms.

We live in the greatest time in human history. Only 200 years ago, for most Europeans, life was a struggle rather than a pleasure. Without antibiotics and hospitals, every infection was fatal. There was only a small elite of citizens who lived in the cities in relative prosperity. Freedom of opinion, human and civil rights were far away. Voting rights and decision-making were reserved for a class consisting of nobility, clergy, the military and rich citizens. The interests of the general population were virtually ignored.