Things are about to get interesting. You've likely heard that Google's DeepMind recently beat the world's best Go player. But in far more practical and pervasive ways, artificial intelligence (AI) is creeping into every aspect of life--every screen you view, every search, every purchase, and every customer service contact. It's the confluence of several technologies--Moore's law made storage, computing, and access devices almost free. This Venn diagram illustrates how deep learning is a subset of AI and how, when combined with big data, can inform enabling technologies in many sectors.
Where does the European insurance industry stand in terms of advanced analytics, AI and automation? Do we see that traditional methods of data analysis are now being labeled by the term "machine learning"? Maybe the industry is more advanced than that: Are real chatbots, for example, already ubiquitous? Let's have a closer look. I had the opportunity to visit the "Insurance AI and Analytics Europe" conference in London.
ODSC gather a large community of Data Scientists around the world, with 3 organizations in Europe, West and East. Apart from the conference they hold every year, they also provide a newsletter, a job board and organize meetups to animate the community. At the ODSC London 2017 there were 10 training sessions, 28 workshops and 75 talks for 1500 attendees. The various topics covered were: Deep Learning, Predictive Analytics, Machine Learning, NLP, Cognitive Computing, AI, and Data Wrangling. Many tools were presented, from Big Data tools such as Apache Spark (SQL, Mllib, Streaming), Hadoop, Apache Storm and Apache Flink, to Deep Learning tools such as Tensorflow, Caffee, Torch, and some well known visualization tools like Neo4J, D3.js, R-Shiny.
The cost of automation: As long as the marginal cost of computerized labour is higher than human labour, it is not cost-efficient to automate. This is currently the case for heterogenous and non-repetitive labour intensive tasks. Yet, one has to bear in mind that computerized labour is becoming more capable and affordable from one development cycle to the next. Wage elasticity: Automation might lead to an excess supply of workers willing to work for less than they are currently being paid. Minimum wages could be deregulated in order to make human labour competitive with computerized labour.
Technology is filled with buzzwords that come and go. However, three key ideas that have grown in stature and relevance over recent years are blockchain technology, artificial intelligence and the internet of things. These three emerging technologies represent different aspects of the data world, and 2018 may be the tipping point in their convergence. Three different pieces of technology will start working together in a seamless ecosystem, and the result is a more connected, more efficient and more secure world. The Internet of Things (IoT) world may be exciting, but there are serious technical challenges that need to be addressed, especially by developers.
Artificial intelligence has conquered games and image recognition, but will it master investing? The short answer is yes, but how soon and how complete? Machine learning methods have had impressive recent successes. These include defeating humans at chess, Jeopardy, poker and Go, as well as providing superior image and speech recognition. Developers strive to create tools that automate decision making and that can mimic or exceed human performance for specific tasks.
The invention of artificial things that learn and perform actions took place in the classic times. Alongside Calculus Ratiocinator by Llull, there were many fictional stories and dramas depicting artificial things and their immense potentials. You must watch it if you haven't. Church-Turing thesis -- which means machines can simulate any process of formal reasoning (from Wiki). Theory that backed up the brains of creators like Allen Newell, Herbert Simon, John McCarthy, Marvin Minsky, and Arthur Samuel.
A survey of 500 chief information officers (CIOs) from around the world by ServiceNow has found that machine learning has arrived in the enterprise, and is making material contributions to everyday work. To realise its full value, technology leaders must find skilled talent to work side-by-side with machines, in addition to redesigning their organisations and processes. CIOs were interviewed in 11 countries across 25 industries, including 46 CIOs in the UK, to uncover the competitive benefits of adopting machine learning and hear how those leaders are driving results. See also: Government CIO I.T. budget breakdown: Gartner IDC estimates that investment in machine learning will nearly double by 2020, and recent analysis shows that machine learning specialists are among the fast-growing roles in IT. Humans are working side-by-side with smart machines for better accuracy, speed and growth of business.
Machine learning has become mainstream, and suddenly businesses everywhere are looking to build systems that use it to optimize aspects of their product, processes or customer experience. The cartoon version of machine learning sounds quite easy: you feed in training data made up of examples of good and bad outcomes, and the computer automatically learns from these and spits out a model that can make similar predictions on new data not seen before. What could be easier, right? Those with real experience building and deploying production systems built around machine learning know that, in fact, these systems are shockingly hard to build. This difficulty is not, for the most part, the algorithmic or mathematical complexities of machine learning algorithms.
The right to due process was inscribed into the US constitution with a pen. A new report from leading researchers in artificial intelligence cautions it is now being undermined by computer code. Public agencies responsible for areas such as criminal justice, health, and welfare increasingly use scoring systems and software to steer or make decisions on life-changing events like granting bail, sentencing, enforcement, and prioritizing services. The report from AI Now, a research institute at NYU that studies the social implications of artificial intelligence, says too many of those systems are opaque to the citizens they hold power over. The AI Now report calls for agencies to refrain from what it calls "black box" systems opaque to outside scrutiny.