The most interesting development of the week had nothing to do with Facebook or even Google losing its appeal against a €2.4bn fine from the European commission for abusing its monopoly of search to the detriment of competitors to its shopping service. The bigger deal was that DeepMind, a London-based offshoot of Google (or, to be precise, its holding company, Alphabet) was moving into the pharmaceutical business via a new company called Isomorphic Labs, the goal of which is grandly described as "reimagining the entire drug discovery process from first principles with an AI-first approach". Since they're interested in first principles, let us first clarify that reference to AI. What it means in this context is not anything that is artificially intelligent, but simply machine learning, a technology of which DeepMind is an acknowledged master. AI has become a classic example of Orwellian newspeak adopted by the tech industry to sanitise a data-gobbling, energy-intensive technology that, like most things digital, has both socially useful and dystopian applications.

Synthetic datasets are increasingly being used to train AI models. These promise greater privacy and less bias, but are not without their drawbacks. Synthetic datasets are becoming increasingly popular for training artificial intelligence models. Proponents of this computer-generated data say it protects personal information and reduces the chances of bias emerging in AI systems. But for many, concerns over privacy and accuracy remain.

Artificial intelligence (AI) is transforming the business landscape of the United States, allowing companies across different industries to streamline operations, optimize systems and leverage big data to make better decisions. Practically every day there seems to be a new breakthrough or a different application for AI, which makes things interesting for businesses that strive to stay ahead of the competition. Some businesses see AI as a magic bullet destined to address all their innovation problems, while others view it as a tool that must be handled with caution, especially given the increasing (and legitimate) attention being paid to the topic of the ethics in AI. While the reality of AI adoption depends on a spectrum of attributes that includes accuracy, explainability, ethics, regulations and technology democratization, to name a few, one thing is for certain: AI is here to stay. With that in mind, here are three AI trends businesses should address to stay competitive in the year ahead.

Overfitting is an issue that occurs when a model shows high accuracy in predicting training data (the data used to build the model), but low accuracy in predicting test data (unseen data that the model has not used before). This can particularly be a problem when it comes to using small datasets in the course of building a neural network. It is possible for the neural network to be of such a size that it "overtrains" on the training data -- and therefore performs poorly when it comes to predicting new data. This is to prevent excessive "noise" in the network that artificially increases the training accuracy, but does not result in any meaningful information being transferred to the output layer -- i.e. any increase in the training accuracy comes from excessive training and not from any useful information from the model features themselves. Dropout renders certain nodes in the network inactive as illustrated in the image at the beginning of this article -- thus forcing the network to look for more meaningful patterns that influence the output layer.

Artificial intelligence (AI) is transforming the business landscape of the United States, allowing companies across different industries to streamline operations, optimize systems and leverage big data to make better decisions. Practically every day there seems to be a new breakthrough or a different application for AI, which makes things interesting for businesses that strive to stay ahead of the competition. Some businesses see AI as a magic bullet destined to address all their innovation problems, while others view it as a tool that must be handled with caution, especially given the increasing (and legitimate) attention being paid to the topic of the ethics in AI. While the reality of AI adoption depends on a spectrum of attributes that includes accuracy, explainability, ethics, regulations and technology democratization, to name a few, one thing is for certain: AI is here to stay. With that in mind, here are three AI trends businesses should address to stay competitive in the year ahead.

On this week's episode of my podcast, I Have to Ask, I spoke to Annie Lowrey, a contributing editor at the Atlantic and the author of the new book Give People Money: How a Universal Basic Income Would End Poverty, Revolutionize Work, and Remake the World. It's about universal basic income--the idea that the government would give all its citizens checks every month. Versions of this proposal have caught on with people on the left as well as tech leaders in Silicon Valley and even some hardcore libertarians. Lowrey has written for many years now about economics, but Give People Money is both a reported work--she travels to Kenya, South Korea, and India to view their economic experiments--and a policy brief on what she believes can help alleviate some of the social and political discontent that has arisen from economic change and dislocation. Below is an edited excerpt from the show. In it, we discuss the benefits and drawbacks of UBI, whether or not we should be skeptical that so many Silicon Valley titans have embraced the idea, and how to make the safety net less vulnerable to political attacks.

At the end of the 19th century, the German scientist Paul Ehrlich began to realize that certain chemicals could have highly specific effects on certain diseases. He began to write about the possibility that a drug could act like a magische kugel--magic bullet--that killed only the organism causing disease, and nothing else. Today, scientists are amassing a new arsenal of magic bullets, and new companies are proliferating to carry them forward in the war against cancer and a host of other diseases and disorders. Advances in cell replacement therapy, for example, are making it possible for scientists to genetically engineer a patient's own T-cells so they can specifically target antigens expressed only on the surface of tumor cells. Similar innovations in regenerative medicine and stem cell therapy are likewise opening the way for potentially revolutionary treatments of degenerative eye diseases, heart disease, and neurodegenerative disorders.

Called insitro (a portmanteau of "in silico" and "in vitro"), the new endeavor looks to stem the increasing costs of developing new drugs and, according to Koller, has already found financial backing from ARCH Venture Partners, Foresite Capital, a16z, and Third Rock Ventures. "Our hope at insitro is that big data and machine learning, applied to the critical need in drug discovery, can help make the process faster, cheaper, and (most importantly) more successful," Koller wrote in the post. "To do so, we plan to leverage both cutting-edge [machine learning (ML)] techniques, as well as the profound innovations that have occurred in life sciences, which enable the creation of the large, high-quality data sets that may transform the capabilities of ML in this space." Koller, who is also known for being one of the cofounders of the education technology company Coursera, wrote in her post that insitro hopes to offer a new research pathway for a pharmaceutical industry that has already exhausted the "low-hanging fruit" of medications. As drug research begins to turn toward more specialized treatments, smaller market sizes and more ambiguous patient populations are beginning to take a greater and greater toll on the industry, she explained.

Every time a new, disruptive technology arrives on the scene, there needs to be a way to ensure that it brings real value to the business. With all of the excitement around artificial intelligence, companies are not only concerned about whether AI is capable of delivering such value, but also where they should begin. AI developments are coming fast, and the opportunity to apply AI to solve business problems is real. The areas primed for investment and experimentation in the coming year include recommendation systems, automated customer service, fraud analysis, and automated threat intelligence and prevention systems. AI technologies will quickly expand to other areas.

Every time a new, disruptive technology arrives on the scene, there needs to be a way to ensure that it brings real value to the business. With all of the excitement around artificial intelligence, companies are not only concerned about whether AI is capable of delivering such value, but also where they should begin. AI developments are coming fast, and the opportunity to apply AI to solve business problems is real. The areas primed for investment and experimentation in the coming year include recommendation systems, automated customer service, fraud analysis, and automated threat intelligence and prevention systems. AI technologies will quickly expand to other areas.