As a futurist, it's my job to look ahead and identify the most important future trends in business and technology. However, many of them won't become relevant until decades from now. For more actionable advice and to help business leaders prioritize, I also provide a look into the more immediate future. Every year, I look ahead and outline the key tech trends for the coming year – those that businesses must address today to remain competitive. So, let's take a look at my list of key tech trends that everyone should be ready for.

What is the difference between artificial intelligence and machine learning, and when would you use one over the other?

Traditional search engines use manual tagging or keywords queried against their index to provide results to a customer. This neglects what your customers think, how they behave and what they expect from their search experience. With the evolution of search experiences provided by personalization masters like Google, Amazon and Netflix, customers want the same personalized experience on every website they visit. Natural language search is essential to providing users with the relevant search they crave. It moves beyond keyword matching and programming tedious manual rules.

Were you unable to attend Transform 2022? Check out all of the summit sessions in our on-demand library now! ManageEngine commissioned Vanson Bourne to conduct a global study to examine the role of IT and how it will continue to evolve in the future of work. The report discovered that everyone across the enterprise, not just IT, has a stake in how technology is chosen, deployed, configured and used. Most departments outside IT -- particularly quality control (24%) and finance (21%) -- are using artificial intelligence/machine learning (AI/ML).

Netflix is slowly making good on its promise to develop a pipeline of 50 games by acquiring and hiring from established firms and in premiere locations,

The AI Policy Forum (AIPF) is an initiative of the MIT Schwarzman College of Computing to move the global conversation about the impact of artificial intelligence from principles to practical policy implementation. Formed in late 2020, AIPF brings together leaders in government, business, and academia to develop approaches to address the societal challenges posed by the rapid advances and increasing applicability of AI. The co-chairs of the AI Policy Forum are Aleksander Madry, the Cadence Design Systems Professor; Asu Ozdaglar, deputy dean of academics for the MIT Schwarzman College of Computing and head of the Department of Electrical Engineering and Computer Science; and Luis Videgaray, senior lecturer at MIT Sloan School of Management and director of MIT AI Policy for the World Project. Here, they discuss talk some of the key issues facing the AI policy landscape today and the challenges surrounding the deployment of AI. The three are co-organizers of the upcoming AI Policy Forum Summit on Sept. 28, which will further explore the issues discussed here. Q: Can you talk about the ongoing work of the AI Policy Forum and the AI policy landscape generally?

In the interim, other events, such as The Game Awards and Summer Game Fest have grown in prominence. Both are hosted and organized by Geoff Keighley, a longtime host at E3 until 2020 when he announced that he would not be returning to the trade show, citing concerns about its lack of innovation. Keighley's Summer Game Fest in 2021 was a resounding success: The show received over 25 million live streams with a peak of 3 million concurrent viewers globally, according to figures shared with The Washington Post, and it hosted the first gameplay reveal of FromSoftware's mega hit, "Elden Ring." Despite both Summer Game Fest and E3 both being slated for June 2023, Keighley has maintained that the two events are not competitors.

The development of medical applications of machine learning has required manual annotation of data, often by medical experts. Yet, the availability of large-scale unannotated data provides opportunities for the development of better machine-learning models. In this Review, we highlight self-supervised methods and models for use in medicine and healthcare, and discuss the advantages and limitations of their application to tasks involving electronic health records and datasets of medical images, bioelectrical signals, and sequences and structures of genes and proteins. We also discuss promising applications of self-supervised learning for the development of models leveraging multimodal datasets, and the challenges in collecting unbiased data for their training. Self-supervised learning may accelerate the development of medical artificial intelligence.

The genome of a eukaryotic cell is often vulnerable to both intrinsic and extrinsic threats owing to its constant exposure to a myriad of heterogeneous compounds. Researchers developed Metabokiller, an ensemble classifier that accurately recognizes carcinogens by quantitatively assessing their electrophilicity, their potential to induce proliferation, oxidative stress, genomic instability, epigenome alterations, and anti-apoptotic response. The development of medical applications of machine learning has required manual annotation of data, often by medical experts. Yet, the availability of large-scale unannotated data provides opportunities for the development of better machine-learning models. In this Review, the authors highlight self-supervised methods and models for use in medicine and healthcare, and discuss the advantages and limitations of their application to tasks involving electronic health records and datasets of medical images, bioelectrical signals, and sequences and structures of genes and proteins.

What do the recent AI breakthroughs, DALLE[1] and Stable Diffusion[2] have in common? Hence, if you want to grasp how those models work, understanding CLIP is a prerequisite. Besides, CLIP has been used to index photos on Unsplash. But what does CLIP do, and why it's a milestone for the AI community? CLIP is an open source, multi-modal, zero-shot model.