AI, so hot right now


This blog post was originally posted at TechCrunch.

Artificial intelligence is one of the hottest subjects these days, and recent advances in technology makes AI even closer to reality than most of us can imagine. The subject really got traction when Stephen Hawking, Elon Musk and more than 1,000 AI and robotics researchers signed an open letter issuing a warning regarding the use of AI in weapons development last year. The following month BAE Systems unveiled Taranis, the most advanced autonomous UAV ever created, and there are currently 40 countries working on the deployment of AI in weapons development. The defense industry are not the only ones engaging in an arms race to create advanced AI.

Tech giants Facebook, Google, Microsoft and IBM are all engaging in various AI-initiatives as well as competing on developing digital personal assistants like Facebook’s M, Cortana from Microsoft and Apple’ Siri. Mark Zuckerberg even wants to create his own version of Jarvis from Iron Man to run his home. At this year’s World Economic Forum in Davos it was stated that artificial intelligence is ushering in the fourth industrial revolution that will change society as we know it and cost five million jobs by 2020.

Robots is no longer limited to traditional blue-collar jobs, fully automated assembly lines and high frequency trading algorithms. White-collar jobs are ripe for automation and robots are replacing bank tellers, mortgage brokers and loan officers in the financial industry. These examples follow strict repetitive rule-based routines and a machine easily performs that without any human interaction.

However, recent development is the beginning of a new era of AI that are able to perform complex tasks and no longer rely on pre-programmed rules in decision-making. Robo-advisor services like Betterment and Wealthfront are rising in popularity, and the hedge fund industry is launching AI-controlled funds that operates completely without human interaction. The co-head of one of these funds predicts that the time will come that no human investment manager will be able to beat the computer. But how is it possible for an AI to operate autonomously without any human interaction?

Machine learning is one of the fundamentals behind AI and was defined by Arthur Samuel back in 1959 as the science of getting computers to learn and act without being explicitly programmed. This technology is integral in the development of self-driving cars, IBM Watson, speech- and image recognition, as well as solving some of our most challenging tasks like making sense of the human genome. Machine learning has its roots from statistical pattern recognition, and is fundamental in many everyday applications and services like spam filters and web search algorithms. The fundamentals of machine learning is letting the computer program learn from examples. In order to accelerate machine learning development, Google released its machine learning system, Tensorflow on Github which led to Microsoft following up shortly after.

Deep learning takes the concept of machine learning even deeper (pun intended) and can model complex non-linear relationships consisting of many layers. Deep learning is often mentioned interchangeably together with artificial/deep neural networks, which can be viewed as biologically inspired programming paradigm, which enables a computer to learn from observational data. Deep learning is considered the techniques we apply to learn in neural networks.

Quantum computing is the latest and hottest in AI development and Google states that they have in collaboration with NASA a quantum Computer that is 100 million times faster than a traditional computer. The D-Wave 2X could theoretically complete calculations within seconds to a problem that might take a digital computer 10,000 years to calculate. Although Google states that quantum computing might not be suitable for deep learning. While traditional computers rely bits that are either 1 or 0, a quantum computer is based on qubits that can hold a superposition and be both 1 and 0 simultaneously. This state enables quantum computers to crunch data at an exponential rate. While quantum computing may not be suited for deep learning, it could revolutionize the field of optimization in logistics, investment strategies and energy production and consumption.

I have limited this post to include only a selection of the technologies and techniques applied in AI research and development. The road to artificial intelligence is no single discipline but a collection of specialized subject matters, techniques and theories that together interacts to create some form of intelligence. I have limited this post to include only a selection of the technologies and techniques applied in AI research and development. For further insight, I recommend looking into the online course offered by Google at Udacity on deep learning, the class on machine learning at Stanford Open Classroom as well as  evolutionary computing and logic programming (even though I still hold a grudge against Prolog for making me feel too stupid to really understand how it works).

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