Large language models (LLMs) based on transformer architectures have taken the world by storm, with ChatGPT quickly becoming a household name. While the concept of generative AI is not new and can be traced back to Jürgen Schmidhuber's (now at KAUST) work in the 1990s and even further into history, Ian Goodfellow's generative adversarial networks (GANs) and Google's transformers published in 2017 enabled the development and industrialization of multi-purpose AI. My teams have been working in this area since 2015 both in generative biology and generative chemistry, with AI-generated drugs in human clinical trials and the most advanced departments in pharma companies using our software, and we have utilized LLMs almost since they were first published. OpenAI's GPT has also been available to the public since 2020. However, the public release and consumerization of ChatGPT have taken the world by surprise and triggered a new cycle of hyper investment and productization of LLMs that are propagating into the search market. Although both Recurrent Neural Network (RNN) and transformer-based LLMs, as well as multimodal LLMs, are surprisingly good at language understanding and generation, I believe they are still as far from human-level consciousness as a calculator.

Employee Experience is a 21st century reality that no organization, big or small, can escape. But it is a concept that has been forced to be reinvented time and again because of the changing nature of workplace, technology and the shifting definition of an'employee'. Today, many employees are working from remote locations, or are project based and contractual or are simply not human! How then, can organizations uniformly extend an employee experience aligned with their employer brand and culture? The Consumerization of Artificial Intelligence in HR is addressing questions such as these and much more.

Consumerization is the design, marketing, and selling of products and services targeting the individual end consumer. Apple CEO Tim Cook recently promoted a $100-per-year iPhone app called Derm Expert. Derm Expert allows doctors to diagnose skin problems using only their iPhone. Doctors take a photo of a patient's skin condition and then Derm Expert diagnoses the problem and prescribes treatment. Doctors can effectively treat patients without a high performance computer or an expensive technology environment.

These predictive capabilities of AI (existent across applications involving more than just digital assistants including those for healthcare and autonomous vehicle deployments) are widely facilitated by deep learning algorithms. The general nature of machine learning algorithms is to increase their effectiveness with the more data processed. Deep learning, which also includes neural network algorithms specifically designed to mimic the human brain, encompasses an assortment of data to achieve this functionality; depending on the use case autonomous vehicles and virtual assistants can process data regarding the time, the weather, geography, and personal information about the user.

There is a big role artificial intelligence can play in helping to shape the future of web-driven consumer healthcare and that future is happening now. The ubiquity of artificial intelligence is indisputable. Manifestations of machine learning, deep learning and neural network algorithms are deployed in almost every vertical industry, from equipment asset monitoring to real-time financial services analysis. Nonetheless, the future of these technologies likely depends on their consumerization in the form of mainstream adoption by the general public. It's what artificial intelligence can--and is--doing in the personal, as opposed to professional, lives of users which represents its most exciting developments today.

Is that an AI in your pocket or are you just happy to see me? The consumerization of IT describes the reversal of traditional enterprise technology adoption. What was once a centralized top-down process where the IT department had sole control over what technology was used in the enterprise, we're now far more likely to see the opposite, a decentralized bottom-up process where individual employees influence what technology is used due to the choices they make as consumers. After all, there are far more consumers than there are enterprise employees. Technology companies tend to pursue success in the general marketplace rather than struggle to find adoption in the enterprise (which becomes far easier once consumers have decided to love you and your products).

Robots are expected to put many people out of work in the coming years. But before manufacturing and service jobs become totally automated, robots need to win our hearts and minds. According to the most recent edition of From Internet to Robotics, a comprehensive report on the rise of automatons from researchers at Yale, Carnegie Mellon, and other schools studying the robotic shift, robots need to clear several technological and psychological hurdles before they're really accepted in the workplace. To start, robotic hands need to be more dexterous, and controlling them needs to get much easier; but above all, human workers will need to feel like the robots are their friends. The easiest way to do that is through consumerization, or developing automatons for the masses, and using that process to raise more awareness of the benefits robots can offer.

With the enormous wave of machine intelligence technologies comes yet another hype curve of products that cover virtually every key product segment in the industry. Machine intelligence (MI) is no longer the sibling of artificial intelligence or what was pejoratively referred to as "machine intelligence as a magic box." When MI is paired with Internet of Things (IoT) and big data deployments, there are precious few things in enterprise or government computing that aren't touched by these platforms. In this environment, it becomes more difficult to say what MI is not rather than what it is. Exacerbating this fuzziness is the challenge that machine intelligence has had such a checkered history, leading many to wonder whether this iteration, unlike its predecessors, is going to stick.