As a graduate student doing his master's thesis on speech recognition at the MIT AI Lab (now the MIT Computer Science and Artificial Intelligence Laboratory), Dan Huttenlocher worked closely with Professor Victor Zue. Well known for pioneering the development of systems that enable an user to interact with computers using spoken language, Zue traveled frequently to Asia -- where much of the early research in speech recognition happened during the 1980s. Huttenlocher occasionally accompanied his professor on these trips, many of which involved interactions with members of MIT Industrial Liaison Program, as he recalls. "It was a tremendous opportunity," according to Huttenlocher, "and it was a large part of what built my interest in engaging with companies and industry in addition to the academic side of research." Huttenlocher went on to earn his PhD in computer vision at the Institute and has since embarked on a career that encompasses academia, industry, and the philanthropic sector.

The term meta, by its most modern definition can be described as self-referencing or self-reflective. In contemporary nomenclature, meta is often used as a standalone adjective. A "meta" name for a dog would be Dog or a meta movie – would be a movie about movies. And so, we have the metaverse. Another world for people and businesses to inhabit to conduct transactions and interact without the necessity of being fully, physically present.

Lots of businesses utilize large information to improve their operations. E-commerce businesses employ qualitative and probabilistic procedures to venture off cybersecurity risks while mining huge amounts of customer information to construct recommendation engines. Targeted marketing campaigns geared toward providing a personalized customer experience. But since the usage cases for information science grow more complicated, a few innovative startups are currently relying on artificial intelligence and machine learning to their core product offering or business model–and in doing this, attaining things that would not be possible without information. More than 12,000 startups recorded on Crunchbase rely upon machine learning due to their primary and ancillary services and products.

With the recent advances in technology, it's hard to know where to put your attention. For example, 5G hasn't taken off as fast as people would have hoped, but the possibility of combining it with artificial intelligence (AI) may lead to considerable innovations in the next few years. A decade from now, the combination of AI and 5G networks will have revolutionized how business gets done in our everyday lives. They'll receive this requested information almost instantaneously due to the vast bandwidth provided by 5G. This high-speed data connection will open up new opportunities.

"Neural networks represent the beginning of a fundamental shift in how we write software. The current coding paradigms nudge developers to write code using restrictive machine learning libraries that can learn, or explicitly programmed to do a specific job. But, we are witnessing a tectonic shift towards automation even in the coding department. So far, code was used to automate jobs now there is a requirement for code that can write itself adapting to various jobs. This is software 2.0 where software writes on its own and thanks to machine learning; this is now a reality. Differentiable programming especially, believes the AI team at Facebook, is key to building tools that can help build ML tools. To enable this, the team has picked Kotlin language. Kotlin was developed by JetBrains and is popular with the Android developers. Its rise in popularity is a close second to Swift. Kotlin has many similarities with Python syntax, and it was designed as a substitute for Java.

Government is ushering in a new era of work, using automation and artificial intelligence to help the federal workforce achieve higher levels of productivity and decision-making. Over the past two years, agencies have focused on shifting the workforce to "high-value" work -- a key goal of the President's Management Agenda -- by taking advantage of robotic process automation and other technologies to reduce error, improve compliance and eliminate repetitive administrative tasks. Although RPA is a useful IT capability that allows agencies to eliminate low-value, mundane, transactional work, it can only make simple decisions. By adding AI to the equation, agencies can accelerate the ability of RPA to complete a multitude of tasks at once. This can be particularly helpful when analyzing large swaths of data, enabling decision-makers to meet goals more efficiently and effectively.

Today, artificial Intelligence (AI) helps you shop, provides suggestions on what music to listen to and what shows to watch, connects you with friends on social media and even drives your car. As more companies focus their efforts on AI-based solutions, 2020 is shaping up to be a turning point as we begin to witness the third wave of AI -- when AI systems not only not learn and reason as they encounter new tasks and situations, but have the ability to explain their decision making. The first wave of AI focused on enabling reasoning over narrowly defined problems, but lacked any learning capability and poorly handled uncertainty. Financial products like Turbotax and Quickbooks, for example, are able to take information from a situation where rules have previously been defined and work through it to achieve a desired outcome. However, they are unable to operate beyond the previously defined rules.

With the proliferation of smart technologies and analytics, companies are increasingly realizing the potential of artificial intelligence (AI) and big data. Enterprises are witnessing the growing consensus that AI and big data are closely interweaved, thereby the increasing amount and availability of data is empowering AI initiatives in enterprises. Organizations today have huge volumes of data at their disposal that can feed to determine behaviors and detect patterns and anomalies. Business executives observe that investments in the AI and analytics area besides big data have started to yield significant results. Leveraging the melded competence of AI and big data, organizations can now considerably boost their decision-making capabilities, reduce expenses, and achieve a much higher success rate.

And you need some information about the clients you will be meeting for the first time. Power up your tablet and download a spreadsheet that is more spread than sheet. And you start searching and searching and searching for the pertinent information. But what if what you are looking for is not part of the standard ledger? There's no one in the car but you, but you are dying to ask a question.

Frank Casale, Founder of Institute for Robotic Process Automation sits down with Naresh Kothari, AVP & Head – Business Development, EdgeVerve (Americas), in a two-part video interview to discuss how AI-based systems are ushering in the next wave -- going beyond RPA -- to deliver enterprise efficiencies and transformation. Check out the videos for expert insight and advice on which questions to ask before embarking on your automation journey.