Some ultra-processed foods are packed with artificial colours and flavourings. But scientists have found that food which is 100 per cent artificial looks even tastier than the real deal. Researchers from the University of Oxford and the University of Naples Federico II discovered that people rate AI-generated food images are more appetising. And, as AI images become more realistic, the experts warn that they could even promote unhealthy eating habits. So, can you separate the real photos from their AI copies?

Until this point, there's the awareness that AI can automate many tasks, increase productivity and make our lives easier while also enabling us to solve problems that are simply too complicated for humans to solve alone. However, AI is not without its challenges. As we see AI becoming increasingly pervasive in our society and affecting every aspect of our lives, it also creates a host of new ethical and moral questions that have never been faced before. This means there's no real way for consumers to protect themselves from having their data misused by companies or being discriminated against due to the biases programmed into machine learning systems. It also means that companies have little incentive to use their customers' data responsibly.

Following bold claims about the business benefits and even world-saving power of artificial intelligence (AI), it's not surprising that talk of broken promises is growing. However, has AI failed to deliver, or are we setting our expectations too high? Recently, consternation has centered around Covid-19 as some initial AI-assisted breakthroughs quickly fizzled out. For many, frustration stretches back much further, with a 2010 study showing 38% of organizations lacked the understanding of how to use analytics to make better and faster decisions. Today, fewer than 25% of global organizations have developed an enterprise-wide AI strategy.

Following bold claims about the business benefits and even world-saving power of artificial intelligence (AI), it's not surprising that talk of broken promises is growing. However, has AI failed to deliver, or are we setting our expectations too high? Recently, consternation has centered around Covid-19 as initial AI-assisted breakthroughs from the likes of C3.ai quickly fizzled out. For many, frustration stretches back much further, with a 2010 study showing 38% of organizations lacked the understanding of how to use analytics to make better and faster decisions. Today, fewer than 25% of global organizations have developed an enterprise-wide AI strategy.

In an industry as dynamic and exciting as the IoT (Internet of Things), it can difficult to separate hype from reality. One sector that often falls victim to hype is AI (artificial intelligence). For many decades, machine intelligence and autonomous decisionmaking have grabbed ahold of people's imaginations, simultaneously bringing about utopian and dystopian predictions for the future AI-driven world. Practically speaking, the hype surrounding AI technologies can make it tough for investors and businesses to get a handle on exactly what to expect from this growing space and how they should try to harness it. How can companies avoid getting caught up in the AI hype cycle?

I just came back from the Venture Beat Transform 2019 conference here in San Francisco, and I have to say, for the first time in many years, I'm starting to feel like the industry is finally starting to get it. What am I talking about specifically? Well, I'm traditionally always right about stuff, and this time, I've been incredibly right about the challenges the market may have had in adopting machine learning. It really felt like everyone at this conference had read these posts and was reporting everything they learned to the audience. That, of course, is exactly what didn't happen.

The past few years have seen significant advancements in computing power. With this, machines seem to have a greater ability to learn about us and participate in our lives. Whether through product purchase suggestions on Amazon.com and other retail outlets or in our business and professional pursuits, machines are busy learning everywhere around us. Recently, the market has become flooded with buzz words relating to this type of work. Artificial Intelligence, Machine Learning, and Deep Learning are often used inaccurately and interchangeably.

THE GIST: Although media headlines imply we are already living in a future where AI has infiltrated every aspect of society, this actually sets unrealistic expectations about what AI can really do for humanity. Governments around the world are racing to pledge support to AI initiatives, but they tend to understate the complexity around deploying advanced machine learning systems in the real world. This article reflects on the risks of "AI solutionism": the increasingly popular belief that, given enough data, machine learning algorithms can solve all of humanity's problems. There is no AI solution for everything. All solutions come at a cost and not everything that can be automated should be.

THE GIST: Although media headlines imply we are already living in a future where AI has infiltrated every aspect of society, this actually sets unrealistic expectations about what AI can really do for humanity. Governments around the world are racing to pledge support to AI initiatives, but they tend to understate the complexity around deploying advanced machine learning systems in the real world. This article reflects on the risks of "AI solutionism": the increasingly popular belief that, given enough data, machine learning algorithms can solve all of humanity's problems. There is no AI solution for everything. All solutions come at a cost and not everything that can be automated should be.

This article is featured in the new DZone Guide to Artificial Intelligence. Get your free copy for more insightful articles, industry statistics, and more! To gather insights on the state of artificial intelligence (AI) and all its variants -- machine learning (ML), deep learning (DL), natural language processing (NLP), predictive analytics, and neural networks -- we spoke with 22 executives who are familiar with AI. The key to having a successful AI business strategy is to know what business problem you are trying to solve. Having the necessary data, having the right tools, and having the wherewithal to keep your models up-to-date are important once you've identified specifically what you want to accomplish.