I explain Artificial Intelligence terms and news to non-experts. Here are the five best HackerNoon articles related to artificial intelligence in February. I hope they will help you learn more about machine learning this year. Note that the five articles you will see were curated by myself amongst hundred of other super interesting ones that you might enjoy even more. So please feel free to look at the AI tag on HackerNoon and keep learning!

Artificial Intelligence is everywhere, opportunities are in abundance for cognitive enterprises. What do we mean by cognitive enterprises? Millions of ideas and think pieces are waiting to grow luxuriantly and cognitive AI technologies will play a bigger role in turning your ideas into a live piece of work. It is expected that AI will bring simplicity to complex business issues and deliver more useful, engaging, intuitive, and profitable solutions, and this is what we say a cognitive approach for enterprises. According to a report published by IDC a market research firm states that global spending on cognitive AI systems will reach $57.6 billion by 2021. Biggest investors in cognitive AI systems are banking, retail, and manufacturing firms.

As is usually the case with fast-advancing technologies, AI has inspired massive FOMO, FUD and feuds. Some of it is deserved, some of it not -- but the industry is paying attention. From stealth hardware startups to fintech giants to public institutions, teams are feverishly working on their AI strategy. It all comes down to one crucial, high-stakes question: 'How do we use AI and machine learning to get better at what we do?' More often than not, companies are not ready for AI.

Have you ever wondered about this concept called Mind reading? It might be a myth right. But a team of scientists led by neurosurgeon Edward Chang of University of California, San Francisco (UCSF) has developed an Artificial Intelligence (AI) that can converts someone's brain thoughts into text. Their study was published in Nature Neuroscience. In the study, in which four patients with epilepsy wore the implants to monitor seizures caused by their medical condition, the UCSF team ran a side experiment: having the participants read and repeat a number of set sentences aloud, while the electrodes recorded their brain activity during the exercise.

Deep Learning is a revolutionary field, but for it to work as intended, it requires data. The area related to these big datasets is known as Big Data, which stands for the abundance of digital data. Data is as important for Deep Learning algorithms as the architecture of the network itself, i.e., the software. Acquiring and cleaning the data is one of the most valuable aspects of the work. Without data, the neural networks cannot learn.

Not so long ago, AI startups were the new shiny object that everyone was getting excited about. It was a time of seemingly infinite promise: AI was going to not just redefine everything in business, but also offer entrepreneurs opportunities to build category-defining companies. A few years (and billions of dollars of venture capital) later, AI startups have re-entered reality. Time has come to make good on the original promise, and prove that AI-first startups can become formidable companies, with long term differentiation and defensibility. In other words, it is time to go from "starting" mode to "scaling" mode.

Perceptron is a fundamental unit of the neural network which takes weighted inputs, process it and capable of performing binary classifications. In this post, we will discuss the working of the Perceptron Model. This is a follow-up blog post to my previous post on McCulloch-Pitts Neuron. In 1958 Frank Rosenblatt proposed the perceptron, a more generalized computational model than the McCulloch-Pitts Neuron. The important feature in the Rosenblatt proposed perceptron was the introduction of weights for the inputs.

Narrow AI is where we have been. General AI is where we are going. Narrow AI refers to AI which is able to handle just one particular task. A spam filtering tool, or a recommended playlist from Spotify, or even a self-driving car -- all of which are sophisticated uses of technology -- can only be defined via the term'narrow AI'. Even Watson, IBM's media-friendly supercomputer which can beat human experts at Jeopardy!

Many believed an algorithm would transcend humanity with cognitive awareness. Machines would discern and learn tasks without human intervention and replace workers in droves. They quite literally would be able to "think". Many people even raised the question whether we could have robots for spouses. But I am not talking about today.

Elon Musk has sounded off on AI various times, and how it can pose a massive threat to the world. At a meeting of the nation's governors, Elon Musk warned that artificial intelligence was "the greatest risk we face as a civilization". As a result of his deep involvement in the tech industry, Elon Musk said he "[has] access to the very most cutting edge AI," and that "people should be really concerned about it." In a tweet, Elon Musk stated that AI was "potentially more dangerous than nukes". Elon Musk is also concerned that AI can be used in a warfare scenario, by being able to create massive amounts of fake news (i.e. in a propaganda scheme to push a certain ideology), fake press releases, and manipulating information in general.