AI & Machine Learning

A common view in the press and in artificial intelligence research is that sentient and intelligent machines are just on the horizon. After all, machines diagnose and treat illnesses, drive cars, grow our food, manufacture and deliver new products, distinguish pictures, and play games better than we do. How much longer can it be before they surpass our intelligence and take our jobs? Before we decide if machines can surpass our intelligence, let us first define two terms that will help us getter a better handle on this topic: Weak AI and Strong AI. 

While A/B testing might be a relatively easy-to-execute practice, most marketers will continue to faithfully serve a “winner takes all” approach in absence of being able to handle the heavy-duty analysis required despite knowing it will compromise the experience for a portion of their visitors. By using ad serving-like techniques for changing the onsite experience, instead of doing an A/B test of five different banners or five different call-to-actions, marketers can create all the variations they need and let a real-time machine learning engine do the work.

Real estate is a field that might sound like a strange fit for AI applications. It might even sound like a possible threat to your business model or livelihood. As it turns out, there’s quite a lot of room for overlap here — and more than a few exciting opportunities along the way for humans to work alongside AI in the complex, fast-moving real estate world. There’s still no substitute for the human touch and intuition, but with a helping hand from AI, tomorrow’s real estate agents will be even more effective, knowledgeable and organized.

Will artificial intelligence bring a utopia of plenty? Or a dystopic hellscape? Will we, jobless and destitute, scavenge for scraps outside the walls of a few techno-trillionaires? Or will we work alongside machines, achieving new levels of productivity and fulfillment? The tech world has no lack of prognosticators. Over the next 5–10 years and beyond, we’ll see in exactly which ways AI revolutionizes industry and business. One thing, however, is clear: It’s happening, and it’s going to be big. 

Neural networks are a ground breaking technology. Like machine learning in general, they are becoming more and more integrated into our lives without many of us realising. They can be wildly complex and it is very difficult to understand how they come to decisions. As the name suggests the inspiration behind their design comes from something we all own and use every day; the brain.

Machine Learning has made great advances in pharma and biotech efficiency. AI is also helping us more efficiently diagnose diseases, develop drugs, personalize treatments, and even edit genes. But this is just the beginning. The more we digitize and unify our medical data, the more we can use AI to help us find valuable patterns — patterns we can use to make accurate, cost-effective decisions in complex analytical processes. Machine Learning is particularly helpful in areas where the diagnostic information a doctor examines is already digitized. This post summarizes the top 4 applications of AI in medicine today.

One aspect that seems to be conspicuous by its absence is product definition of AI powered software. The reason is not too difficult to fathom. Defining AI products is hard, and there are no industry standard methodologies to speak of. As with any new paradigm shifting technology, it takes some time before the business processes can catch up. AI is no different. In the long run, I believe that a basic understanding of AI within the wider product development community is the solution.

Assistants and bots have reached a new adoption high.  However, many businesses are finding their projects harder to scale than they expected. The disappointment with some deployments triggered controversy whether to use Artificial Intelligence (AI)-powered assistants or to stick with rule-based bots. Assistants and bots are relatively easy to set-up. Companies can start rapidly but are finding it more difficult to scale and can become disappointed. Challenges maximizing conversational AI should not be construed as the technology not being ready for large deployments. Let’s explore what needs to be done to get the most out of conversational AI.

How can machine learning help theoretical science? Machine learning can provide the mathematical scaffolding for scientific theories, to which theorists will then add meaning and the bridge to reality. However, before we can get there we will need to develop a much better understanding of machine learning. We will need to understand machine learning algorithms from general principles. Perhaps, it is time to start developing a real theory of machine learning.

People view the concept of artificial intelligence as one of humanity trying to play the role of God and being doomed to extinction rooted in hubris once the creation surpasses the creator. The reality, of course, is that we are using AI all the time now, at least in some form or another. AI has revolutionized almost every industry and will continue to do so. In this article, we will examine how AI has changed the app development, travel, debt, retail, and IT industries so that we may better understand how AI interacts with business.

We can outline the typical process used in Machine Learning. This process is designed to maximize the chances of learning success and to effectively measure the error of the algorithm. A subset of real data is provided to the data scientist, and he experiments with a number of algorithms before deciding on those which best fit the training data. A further subset of real data is provided to the data scientist with similar properties to the training data. This is called the validation set. The algorithm is run on the test set and the error is calculated.