This article is part of a VB special issue. Enterprise companies have experimented with artificial intelligence (AI) for years -- a pilot here, a use case there. But company leaders have long dreamed of going bigger, better and faster when it comes to AI. That is, applying AI at scale. The goals of this quest may vary.

“Build a talent pool of industry domain and technical experts like data engineers, data scientists, and machine learning engineers.” Develop a data …

You want success over the coming months and years? The number-one way to get there is through people -- building businesses through their creativity, passion, and full participation in decision-making. But right behind empowered people is the number-two vital ingredient for success: data. Data that can reveal to you what your customers want, how your business is running, and what's around the corner. Now, we have the key that unlocks the patterns that have long been hidden away in databases and applications.

At its re:Invent conference, AWS today announced Amazon DataZone, a new data management service that can help enterprises catalog, discover, share and -- most importantly -- govern their data. The nifty part here is that AWS is using machine learning to help businesses build these data catalogs and generate the metadata to make it searchable. "To unlock the full power, the full value of data, we need to make it easy for the right people and applications to find, access and share the right data when they need it -- and to keep data safe and secure," AWS CEO Adam Selipsky said in today's keynote. The tool will provide users with fine-grained controls to manage and govern this data. That's long been a major problem for enterprises, but it has only gotten harder as the amount of data has increased, ensuring that the right users have access to the right data, without compromising personally identifiable information, for example.

Over the past year, machine learning and artificial intelligence technology have made significant strides. Specialized algorithms, including OpenAI's DALL-E, have demonstrated the ability to generate images based on text prompts with increasing canniness. Natural language processing (NLP) systems have grown closer to approximating human writing and text. And some people even think that an AI has attained sentience. And as Ars' Matt Ford recently pointed out here, artificial intelligence may be artificial, but it's not "intelligence"--and it certainly isn't magic.

Hear from top leaders discuss topics surrounding AL/ML technology, conversational AI, IVA, NLP, Edge, and more. Machine learning (ML) has broad applications -- and supervised ML, particularly, has taken off in recent years. Thus, it's critical that organizations build data engines that utilize the right data at the right stage of their projects' lifecycles, Manu Sharma told the audience at VentureBeat's Transform 2022 event. The founder and CEO of Labelbox explained that the "fundamental premise" of supervised ML is creating annotated or labeled data. This involves applying semantic annotations on any unstructured information, such as text and video.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The combination of Wi-Fi 6 and 5G mobility, combined with an increasingly wired and mobile world of internet of things (IoT) technology, promises to bring billions more devices onto networks in the coming years. This will have a profound impact on workplaces of the future, in ways that go far beyond the clear trends of remote employees and hybrid workforces. The world is entering a place where many people can seamlessly connect with fellow workers virtually from any location, with the workplace becoming more intelligent and hoteling becoming the norm. Examples include the ability to schedule a desk similar to seats at the movies or a flight, as well as the ability to crowdsource the temperature in the office.

We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. The heat of July is upon us, which also means we're exactly halfway to 2023. So, it seems like a good time to pause and ask: What are the biggest AI trends so far in mid-2022? The colossal AI trend that all other AI trends serve is the increased scale of artificial intelligence in organizations, said Whit Andrews, vice president and distinguished analyst at Gartner Research. That is, more and more companies are entering an era where AI is an aspect of every new project.

Is deep learning really going to be able to do everything? Opinions on deep learning's true potential vary. Geoffrey Hinton, awarded for pioneering deep learning, is not entirely unbiased, but others, including Hinton's deep learning collaborator Yoshua Bengio, are looking to infuse deep learning with elements of a domain still under the radar: operations research, or an analytical method of problem-solving and decision-making used in the management of organizations. Machine learning and its deep learning variety are practically household names now. There is a lot of hype around deep learning, as well as a growing number of applications using it.

Bitfount is a federated analytics and machine learning platform that makes extracting value from sensitive data easy, fast, private, and secure. For data custodians and data scientists or researchers partnering to achieve better insights from data, Bitfount combines the best of data collaboration design, with advanced privacy-preserving capabilities, while playing nicely with all of your existing tools and crucially not requiring the transfer of any raw data. Data collaboration today is a painful, messy business. As anyone who has attempted to set up a collaboration around sensitive data will know, the current process is generally very painful and slow. Valuable datasets languish in silos as a result of regulatory or commercial sensitivity concerns, incompatible data management solutions, lengthy contractual processes, or just plain lack of understanding of what data is available for which purposes within an organisation.