In a recent IDC spending guide titled Worldwide cognitive systems and artificial intelligence spending guide, some fantastic numbers were thrown out in terms of opportunity and growth 50 % CAGR, Verticals pouring in billions of dollars on cognitive systems. One of the key components of cognitive systems is Machine Learning. According to wikipedia Machine Learning is a subfield of computer science that gives the computers the ability to learn without being explicitly programmed. Just these two pieces of information were enough to get me interested in the field. After hours of daily searching, digging through inane babble and noise across the internet, the understanding of how machines can learn evaded me for weeks, until I hit a jackpot.

Want to receive a weekly deep dive into all things auto, transportation, & logistics tech? Click here to subscribe to our auto tech newsletter. Momentum in auto tech is at an all-time high, with investors funding private startups in the field at a record pace. Of course, much of the buzz has revolved around autonomous driving software, with startups like Zoox seeing $200M funding rounds, tech corporates looking to capitalize, and major automakers working feverishly to catch up. Validating the reliability of fully autonomous vehicles will be no small feat, with RAND estimating that tens or hundreds of billions of test miles might have to be driven to properly gauge their safety. While many players are meeting this challenge head-on, a number of other startups are also developing autonomous tech for more focused applications.

When I tell people that I work at an AI company, they often follow up with "So what kind of machine learning/deep learning do you do?" This isn't surprising, as most of the market attention (and hype) in and around AI has been centered around Machine Learning, and its high profile subset Deep Learning, and around Natural Language Processing, with the rise of the chatbot and virtual assistants. But while machine learning is a core component for artificial intelligence, AI is in fact more than just ML. So what does it really mean for an application to be "intelligent"? What does it take to create a system that is "artificially intelligent? In the real world, the late and great Alan Turing came up a test to measure whether a machine is able to exhibit behaviour is that equivalent to that of a human, aptly known as the Turing Test.

Gloomy predictions abound that the applications of artificial intelligence and machine learning will put huge numbers of people out of work in the coming years. But the corollary is that these technologies create opportunities to develop new goods and services that will bring new jobs. What's certain is that advanced implementations of computer science are beginning to disrupt our lives. We must start thinking about how these technologies are applied and regulated if we are to reap the benefits and minimise potential harms. The introduction of the steam engine in the 18th century disrupted the life of the agricultural labourer and fuelled the rise of cities, creating new industries and new jobs. Traditional professions such as medicine and law were largely unchanged. But the latest industrial revolution has the potential to change almost every form of work.

Corporate giants like Google, IBM, Yahoo, Intel, Apple and Salesforce are competing in the race to acquire private AI companies, with Ford, Samsung, GE, and Uber emerging as new entrants. Over 200 private companies using AI algorithms across different verticals have been acquired since 2012, with over 30 acquisitions taking place in Q1'17 alone (as of 3/24/17). This quarter also saw one of the largest M&A deals: Ford's acquisition of Argo AI for $1B. In 2013, Google picked up deep learning and neural network startup DNNresearch from the computer science department at the University of Toronto. This acquisition reportedly helped Google make major upgrades to its image search feature.

Christoffer O. Hernæs is chief digital officer of Skandiabanken, Norway's first pure internet bank and leading challenger bank. As global poverty continues to decline, another issue emerges. According to the World Economic Forum, rising income inequality and the polarization of societies pose a risk to the global economy, and may lead to increased polarization and lack of political stability. This, however, is not a global problem. In developing countries, inequality is decreasing and the amount of people living in extreme poverty is at an all-time low.

To function effectively in this knowledge economy, you need to read through trillions of data points. You basically need to go back to school every day to answer the questions that hit your desk. That is the challenge of the knowledge economy. And that is where I believe we can do something different in security with AI and cognitive.

When I tell people that I work at an AI company, they often follow up with "So what kind of machine learning/deep learning do you do?" This isn't surprising, as most of the market attention (and hype) in and around AI has been centered around Machine Learning, and its high profile subset, Deep Learning, and around Natural Language Processing, with the rise of the chatbot and virtual assistants. But while machine learning is a core component for artificial intelligence, AI is in fact more than just ML. So what does it really mean for an application to be "intelligent"? What does it take to create a system that is "artificially intelligent?

Arithmetically, the problem is a combination of collapsing productivity and insufficient capital investment. On February 19, 2017, the New York Times ran a feature story on recent changes in the United States oil industry.2 The focus was on the recent "embrace" of technological innovation in the industry after the 2014 plunge in the global oil market. This was just one of a rash of such pieces in the popular press, relying, as is typical of such writing, on a smattering of skewed, decontextualized data, a healthy serving of the anecdotal, and a host of the worst tech journalism clichés ("a few icons on a computer screen," "a click of the mouse," video game marathons as job training, a compulsory reference to drones). Zeroing in on the effects of these changes on workers in west Texas, the article's upshot is unobjectionable enough: as oil prices recover, output rises, and production becomes more capital-intensive, many workers who lost jobs in the downturn will be replaced by machines. These workers, often Latino, are sure to be forced out of these semi-skilled, relatively well-paid jobs into other sectors of the labor market, where their skills and experience will serve little purpose. At first blush, the situation seems dire. We are told that some 30% of jobs in the industry were lost after the oil market crash of mid-2014, when employment in the industry was at its peak.

Artificial intelligence is likely to bring benefits in post-trade processing for capital markets before distributed ledger technology such as blockchain is widely adopted. Vijay Mayadas, head of strategy and fixed income at Broadridge, told Markets Media that post-trade processing in capital markets generates huge amounts of data so workflows will benefit from intelligent automation and machine learning. He said: "There is huge interest in tackling the 5% of exceptions that cause 95% of settlement failures and costs. We are running a number of pilots and expect adoption sooner than blockchain." Mayadas is responsible for strategy, acquisitions, partnerships and growth-related activities at Broadridge including the firm's blockchain initiatives.