When it comes to artificial intelligence (AI), perhaps very few people can claim they fathered a huge part of it. One such man is Jürgen Schmidhuber. Schmidhuber is considered the"father of very deep learning," and the pioneer of deep learning neural networks. In fact, he built the foundations for many of the AI systems we find in our smartphones today. If anyone can predict how far AI will go in the next couple years, it's him. During a talk at WIRED2016, Schmidhuber presented the future of AI as something beyond just taking over jobs.

Like most sectors, the solar industry is rapidly embracing ways to analyze and crunch data in order to lower the cost of solar energy and to open up new markets for their technology. The rise of data tools--algorithms, machine learning, sensors--are driving investments in, and acquisitions of, solar startups, while entrepreneurs are launching new companies that are using data to solve various solar industry problems. Meanwhile, big companies are spending money on tracking, monitoring and evaluating data from solar projects worldwide, helping to lower the cost of generating energy from the sun. It shouldn't come as a surprise that the solar sector is the latest to embrace the value of data. Other traditionally non-digital sectors, like the auto industry, oil and gas, and agriculture are turning to managing data as a necessity to keep their technology competitive and their companies in business.

"It hurts to love someone and not be loved in return, but what is the most painful is to love someone and never find the courage to let the person know

The MogIA artificial intelligence (AI) platform has successfully predicted its fourth US election in a row, paving the way for the potential of this new technology in the future. This election in particular tested the system's ability to predict the results of a presidential race as its prediction was the opposite of what many pre-election polls indicated, leading some to believe that it would lose its streak of successful predictions. MogIa makes its predictions by utilising 20 million data points from a number of online services including Google, Twitter and YouTube. It then analyses the data it collects related to public engagement from social media posts before making a decision as to who will win. Sanjiv Rai, the founder of the Indian startup Genic.ai that developed MogIA, explained how the system operates: "While most algorithms suffer from programmers' /developers' biases, MogIA aims at learning from her environment, developing her own rules at the policy layer and developing expert systems without discarding any data."

Artificial intelligence is one of those tech terms that seems to inevitably conjure up images (and jokes) of computer overlords running sci-fi dystopias -- or, more recently, robots taking over human jobs. But AI is already here: It's powering your voice-activated digital personal assistants and Web searches, guiding automated features on your car and translating foreign texts, detecting your friends in photos you post on social media and filtering your spam. But as practical uses of AI have exploded in recent years, one critical element remains missing: an industrywide set of ethics standards or best practices to guide the growing field. Now, the industry heavyweights are partnering to fill that gap. Called the Partnership on Artificial Intelligence to Benefit People and Society, the group consists of Amazon, Facebook, Google, Microsoft and IBM. Apple is also in talks to join.

In recent years, there has been a staggering surge in interest in intelligent systems as applied to everything from customer support to curing cancer. Simply sprinkling the term "AI" into startup pitch decks seems to increase the likelihood of getting access to funding. The media continuously reports that AI is going to steal our jobs, and the U.S. government seems as worried about the prospect of super-intelligent killer robots as it is about addressing the highest wealth disparity in the country's history. Comparatively, there has been very little discussion of what artificial intelligence is, and where we should expect it to actually affect business. When people talk about AI, machine learning, automation, big data, cognitive computing, or deep learning, they're talking about the ability of machines to learn to fulfill objectives based on data and reasoning.

Digital tools using machine learning to analyze a person's spoken or written words could be instrumental in aiding mental health clinicians in assessments determining whether that person is suicidal, researchers have found. A new study published in the journal Suicide and Life-Threatening Behavior found machine learning is 93 percent accurate in correctly identifying a suicidal person, and is 85 percent accurate in determining differential diagnosis of mental illness. The study, led by researchers at the Cincinnati Children's Hospital Medical Center, looked at 379 patients who were recruited from three different sites – two academic medical centers and a rural community hospital. "Death by suicide demonstrates profound personal suffering and societal failure," writes lead author Dr. John Pestian, who is also a professor of biomedical informatics and psychiatry at Cincinnati Children's. "While basic sciences provide the opportunity to understand biological markers related to suicide, computer science provides opportunities to understand suicide thought markers."

Simply put, any algorithm that has the ability to learn on its own, given a set of data, without having to program the rules of the domain explicitly, falls under the ambit of Machine Learning. This is different from Data Analytics or Expert systems where, rules, logic, propositions or activities has to be manually coded by an expert programmer. Systems which has ability to learn on its own and progress towards a pre-defined goal, without much of human intervention can be broadly termed as Intelligent Systems. The quality of intelligence can range from an amoeba, algae, ant, armadillo all the way to chimps, humans or beyond. As an example, systems which interact with humans in natural language cannot be built by coding the rules and conversational logic of human language.

Machine learning is defined as a subfield of computer science and artificial intelligence which "gives computers the ability to learn without being explicitly programmed" (source). Although the statistical techniques which underpin machine learning have existed for decades recent developments in technology such as the availability/affordability of cloud computing and the ability to store and manipulate big data have accelerated its adoption. This essay is meant to explore the most popular methods currently being employed by data scientists such as supervised and unsupervised methods to people with little to no understanding of the field. Supervised machine learning describes an instance where inputs along with the outputs are known. We know the beginning and the end of the story and the challenge is to find a function (story teller, if you will) which best approximates the output in a generalizable fashion.

In this blog post I will delve into the brain and explain its basic information processing machinery and compare it to deep learning. I do this by moving step-by-step along with the brains electrochemical and biological information processing pipeline and relating it directly to the architecture of convolutional nets. Thereby we will see that a neuron and a convolutional net are very similar information processing machines. While performing this comparison, I will also discuss the computational complexity of these processes and thus derive an estimate for the brains overall computational power. I will use these estimates, along with knowledge from high performance computing, to show that it is unlikely that there will be a technological singularity in this century. This blog post is complex as it arcs over multiple topics in order to unify them into a coherent framework of thought. I have tried to make this article as readable as possible, but I might have not succeeded in all places.