With all the buzz around big data, artificial intelligence, and machine learning (ML), enterprises are now becoming curious about the applications and benefits of machine learning in business. A lot of people have probably heard of ML, but do not really know what exactly it is, what business-related problems it can solve, or the value it can add to their business. ML is a data analysis process which leverages ML algorithms to iteratively learn from the existing data and help computers find hidden insights without being programmed for. With Google, Amazon, and Microsoft Azure launching their Cloud Machine learning platforms, we have seen artificial intelligence and ML gaining prominence in the recent years. Surprisingly, we all have witnessed ML without actually knowing it.
The last time I applied for a mortgage, I had forgotten how many different pieces of paper and documents I needed to find and hand over to the bank. At the time, I couldn't help but think how irritating this was, and how that surely they had all this data to hand already – especially as I was an existing customer with the bank. What I hadn't really thought about was the process and technology behind the application and reams of paper I was relinquishing. Not until I recently met a company based in the Moorgate WeWork, London. Having written a few of these blogs already I realise that I use terms like "the company" or "that company" a lot, so from now on I will simplify this by using "TechCo" (short hand for "Tech Company").
Whether we know it or not, artificial intelligence (AI) is already steeped into everyday life. It's present in the way social media feeds are organised; the way predictive searches show up on Google; and how music services such as Spotify make song suggestions. The technology is also helping transform the way enterprises do business. Commonwealth Bank of Australia, for instance, has applied AI to analyse 200 billion data points to free up more time so its customer service officers can focus on doing exactly what their title suggests: servicing customers. As a result, the bank has seen a 400% uplift in customer engagement.
Artificial Intelligence (AI) is here today; it's not just the future of technology. It is also not just found in toy robots or Hollywood sci-fi movies. It's embedded in the fabric of your everyday life. Despite AI's promise, certain thinkers are deeply concerned about a time when machines might become fully sentient, rational agents--beings with emotions, consciousness, and self-awareness. "The development of full artificial intelligence could spell the end of the human race," Stephen Hawking told the BBC in 2014.
Forget Killer Robots--Bias Is the Real Danger of artificial intelligence. Machine learning bias, also known as algorithm bias or AI bias, is a phenomenon that occurs when an algorithm produces results that are systematically prejudiced due to erroneous assumptions in the machine learning process. Oscar Wilde once argued that life imitates art more than art imitates life. Strangely, that's proving to be the case when it comes to AI development – but not in the way some had hoped. AI programs are made up of algorithms, or a set of rules that help them identify patterns so they can make decisions with little intervention from humans.
It's been 10 years since the first ever Mario AI Competition, so I return to the world of Super Mario level generation research and catch up one some of the more interesting examples that have arisen in recent years. This video is inspired by the following AI research papers and projects: NOOR SHAKER: http://lynura.com/publications.php It's is supported through and wouldn't be possible wthout the wonderful people who support it on Patreon. You can follow AI and Games (and me) on Facebook, Twitter and Instagram: http://www.facebook.com/AIandGames
Today we want to go a step further and implement product recommendation as well. Product recommendation are widely used and are implemented using so called Recommender Systems. There are different ways of implementing recommendations like those we can see on Amazon or Netflix for example. In our case, we will use a multi-class classifier that depending on the answer provided by the user, it will select the product with the highest probability. Using a classifier allows us to avoid having to store past customer behaviour to train the model.
You might not know it, but deep learning already plays a part in our everyday life. When you speak to your phone via Cortana, Siri or Google Now and it fetches information, or you type in the Google search box and it predicts what you are looking for before you finish, you are doing something that has only been made possible by deep learning. Deep learning is part of a broader family of machine learning methods based on learning data representations, as opposed to task-specific algorithms. It also is known as deep structured learning or hierarchical learning. The term Deep Learning was introduced to the machine learning community by Rina Dechter in 1986, and to Artificial Neural Networks by Igor Aizenberg and colleagues in 2000, in the context of Boolean threshold neurons.
Artificial intelligence is growing exponentially. There is no doubt about that. Self-driving cars are clocking up millions of miles, IBM Watson is diagnosing patients better than armies of doctors and Google Deepmind's AlphaGo beat the World champion at Go – a game where intuition plays a key role. But the further AI advances, the more complex become the problems it needs to solve. And only Deep Learning can solve such complex problems and that's why it's at the heart of Artificial intelligence.