Machines are Learning About You. Are You Learning About Them?

        

"If you need to repeat a set of steps, you can automate it"

goes the popular saying amongst technologists. From playing Bach to shooting hoops, machines (also known as bots) are doing it better than Bach and Jordan on their best days.

Before you start drinking that kool-aid and think that your jobs are in danger, remind yourself that a bot's greatest strength is also its biggest weakness. They're highly specialized entities. Sure, they can play play Cantata No. 21 flawlessly but, they can play that only on a specific type of piano. Change the piano and the bot's as good for nothing as you feared you might become.

Some developers out there are dabbling in computer vision and similar streams to get these bots to become a bit more generalized - and that means sacrificing some of that specialization for programming other skills into their chips, or brains, or CPUs (essentially the same thing).

For example, MABL, our newsletter sponsor this week, uses machine learning to perform automated testing of code for bugs, failures, and identifying regressions. Do check them out.

Artificial Intelligence, Machine Learning, and Data Science are the streams where bots are being programmed to analyze their surroundings and perform course corrections on the fly. The biggest achievements in this direction have been getting robots to climb up and down a flight of stairs or, identify flying objects as birds or not-birds. While you can look outside your window and identify a sparrow from a cuckoo, your bot will struggle to even identify the bird from the bushes.

All being said, machine learning has made tremendous strides and more progress happens each day. Thus, in appreciation of man's desire to free mankind of repetitive labor, Hacker Noon presents its top stories on the capabilities of machines to self-learn.

You can write on Hacker Noon too. Simply click here, create an account, and begin writing along with 8000+ other contributors sharing their knowledge and expertise with the rest of us. Maybe you'll be featured here next week.

Without further distractions, let's get to the best AI/ML/Data Science stories on Hacker Noon, curated for your pleasure.

AI in Five, Fifty and Five Hundred Years — Part One

Taylor Swift used facial recognition to track predators at concerts. She’s not the only one. Facial recognition is rolling out to airports everywhere all over the world and it’s coming to a street corner near you.

Hate it or love it, AI is everywhere.

And it’s just getting started.

Right now AI is a mustard seed.

But from those seeds will grow a wild forest that ripples through every aspect of life from top to bottom.

Read full article or { TWEET THIS} by Daniel Jeffries
 

Artificial Intelligence Vs Machine Learning: What's the difference?

Machine Learning can be defined as a subset of AI or can be termed as an application of Artificial Intelligence. In Machine Learning, machines have the ability to learn on their own without being explicitly programmed.

It allows applications to modify themselves based on data in real-time scenarios.

After digging into the basic overview of Artificial Intelligence and Machine Learning, I bring in the crux of the blog. 

Read full article or { TWEET THIS} by Amyra Sheldon
 

Amazing Examples of AI and Machine Learning Applications

Big data analytics is helping Netflix predict what customers enjoy watching. They are also becoming a content creator rather than just a distributor and using data to determine what content they will invest in.

Neuroscience is the inspiration and foundation for DeepMind which creates a machine that can mimic the thought process in our brains - Google's. Deepmind has succeeded in defeating humans at games. The really intriguing thing at DeepMind, however, is health care applications such as reducing treatment planning time and using machines to help diagnose diseases.

Read full article or {TWEET THIS} by Stacy
 

Living in the world of AI - The Human Transformation

A majority of the companies like Google, Facebook, Amazon, and Apple have started using AI in all aspects of their software and hardware. From categorizing top trending posts on Facebook with the use of AI models/algorithms, to the use of AI chips in phones to process images right there in real time, instead of the request going to servers for processing -- AI is everywhere, and we are just getting started.

This becomes all the more important when organizations use AI in building applications that are going to be used by people from all over the world. This includes apps related to sharing photos, messages, social media and anything that could have an impact on lives of people from various race, sex, religion and culture.

Read full article or {TWEET THIS} by Raj Subramanian
 

What Do Sex and AI Have in Common?

Reproduction is the purpose of a species, first appearing 1.2 billion years ago in the evolution of animals. But it's not our only purpose—or we'd be no different from other mammals. No, we are creative, romantic, and most of all, curious beings reaching for the stars.

This curiosity is the drive for many of our actions, whether it's exploring a partner or the intricate mathematical laws of the universe.

While you might think of AI as a methodological endeavor stemming from the ivory halls of elite education, the concept and drive for AI is universal—the desire to achieve out-of-mind experiences—to be intertwined with something other than ourselves.

Read full article or {TWEET THIS} by Frederik Bussler
 

10 Great Articles On Data Science And Data Engineering

This post will discuss our favorite resources for these topics. Now, most of these courses and books are primers for topics like statistics, Python and data science in general. They really will only provide the base knowledge. At the end of the day, real practical experience is one for the few things that will really train your data science knowledge.

You should learn as much as you can from these resources and then apply for as many internships and entry-level positions as possible and study for interviews.

Read full article or {TWEET THIS} by SeattleDataGuy
 

Artificial Intelligence: beyond the hype

The ability for a computer to ‘see’ is an astonishing achievement. AI-powered systems can ‘understand’ the context of an image or a video in impressive level of detail: they can identify an expanding set of entities — such as persons, named individuals, cars, houses, streets, trees and more — with increasing levels of success.

Given an image or video, algorithms can estimate additional properties such as the number of persons in the picture, their gender, age or even their emotional state.

Read full article or {TWEET THIS} by George Krasadakis
 

Trading Bots vs Humans · Everything you need to know

Let's start with performance because that's the most important factor.

The average Hedge Fund has underperformed buy and hold by 71.7% over the last 10 years (+5.95% per year for Hedge Funds and +13.12% per year for the S&P 500 net returns over the past 10 years).

However, there is a strong outlier; algorithmically traded Funds (aka Quant Funds).

The performance of funds like Renaissance and Citadel create a force to be reckoned with. They have achieved a very impressive annualised net return over the past 10 years are +37.1% and 22.1% respectively.

However, not all have been successful.

Read full article or {TWEET THIS} by Janny
 

Creating neural networks without human intervention

A deep neural network is a pipeline of operations that processes data to identify some pattern as a solution to a specific problem. Deep neural nets have a layered structure, and the ‘thinking’ part happens inside the hidden layers between the input and output. The ‘deep’ indicates that there are several internal layers, so the incoming data goes through more complex transformations, as opposed to simpler artificial neural networks.

There are many applications of deep neural networks, and a very basic example is image recognition. In this case, the neural net would take an image as an input and would try to guess with some probability what objects are on it.

Read full article or {TWEET THIS} by Peter
 

Artificial Intelligence, Machine Learning, and Human Beings

In his book Artificial Intelligence and Human Reason, Joseph Rychlak, a psychologist for whom I have great respect, discusses such differences between computer and human reasoning. In it, he reviews studies which undermined behaviorist (AI-type) models of human learning.

I will summarize this review here, and those interested can find the full review in his chapter “Learning as a Predicational Process."

In 1955, psychologist Joel Greenspoon tested the power of contingent reinforcement to operantly condition people toward certain behaviors. Specifically, Greenspoon asked participants in his study to say any words that came to mind out loud, one at a time.

Read full article or {TWEET THIS} by Amber Cazzell