The Biden Administration's actions came on the heels of the European Union, which last June passed the landmark Artificial Intelligence Act, moving a step closer to formally adopting the first-of-its-kind set of comprehensive rules around regulating AI. The AI Act, which was expected to be adopted early this year, sets four classifications for AI risk, ranging from minimal to unacceptable. Technology classified as an unacceptable risk, for example, would include systems that judge people based on a behavior known as social scoring, along with predictive policing tools, and would be banned. There also will be an EU AI board to oversee the implementation and uniform application of the regulations, which will build on existing GDPR and Intellectual Property legislation. The AI Act "is the first comprehensive regulation addressing the risks of artificial intelligence through a set of obligations and requirements that intend to safeguard the health, safety and fundamental rights of EU citizens and beyond, and is expected to have an outsized impact on AI governance worldwide," wrote Mia Hoffmann, a research fellow at the Center for Security and Emerging Technology (CSET) at Georgetown University.

I can already feel it coming. This is going to be the year of "generative AI" the way 2012 was the year of Instagram. The way every year since 2017 has been the year of "blockchain revolution" or the "pivot to video." Best to get clear-eyed and figure out what all the hype is about. After all, Google and Microsoft (and probably others) are now officially in the race to out-AI one another.

The use of artificial intelligence in business is showing signs of acceleration. Nearly three-quarters of companies are now using AI (31%) or are exploring the use of AI (43%), according to IBM's "2021 Global AI Adoption Index." IT professionals responding to the IBM survey cited changing business needs in the wake of the pandemic as a driving factor in the adoption of AI at their companies. Indeed, 43% said their companies have accelerated AI rollouts as a result of the pandemic. Advances in AI tools have made artificial intelligence more accessible for companies, according to survey respondents.

Nearly 10 years later, Dataiku is helping to operationalize AI across a range of business use cases, from fraud detection and customer churn prevention to predictive maintenance and supply chain optimization. If the final destination is weaving AI capabilities so thoroughly into the fabric of day-to-day work that people forget it's there, enterprises are typically somewhere in the middle of the journey, Douetteau says. To get there, they should look inward. In this "AI From the Front Lines" interview, Douetteau and Romain Fouache, Dataiku's chief revenue officer, speak with Beena Ammanath, executive director of the Deloitte AI Institute, about their vision of AI in the enterprise, the importance of building systemization and trust for AI, and how execution will be more important than innovation in democratizing the technologies. "It's not a technology issue--we can build platforms able to continually process and enhance data and build new AI on top to optimize business processes," Douetteau says.

With talk of artificial intelligence in the enterprise moving from hype to implementation, Deloitte's State of AI 5th Edition research report finds that 94% of business leaders agree that AI is critical to success over the next five years. At the same time, one of the more surprising outcomes is that as AI deployments increase, outcomes are lagging Beena Ammanath, executive director of the global Deloitte AI Institute, told TechRepublic. Although 79% of respondents reported achieving full-scale deployment for three or more types of AI applications--up from 62% last year--the percentage of organizations in the underachiever category (high deployment/low outcomes) rose from 17% last year to 22% this year, the report said. This may be because survey respondents reported varying challenges depending on where they are at in their AI implementation. When starting new AI projects, the top challenge reported was proving AI's business value (37%).

Did you miss a session from MetaBeat 2022? Head over to the on-demand library for all of our featured sessions here. Deloitte released the fifth edition of its State of AI in the Enterprise research report today, which surveyed more than 2,600 global executives on how businesses and industries are deploying and scaling artificial intelligence (AI) projects. Most notably, the Deloitte report found that while AI continues to move tantalizingly closer to the core of the enterprise – 94% of business leaders agree that AI is critical to success over the next five years – for some, outcomes seem to be lagging. For example, 79% percent of respondents reported achieving full-scale deployment for three or more types of AI applications, which is up from 62% last year.

Artificial intelligence, better known as AI, sounds like something out of a science-fiction movie. It brings to mind self-aware computers and human-like robots that walk among us. And while those things are part of the overarching artificial intelligence definition and may exist in the future, AI is already a big part of our everyday lives. So, what is artificial intelligence, exactly? It's complicated, but every time you use Siri or Alexa, you're using AI, and that's just the beginning of its practical applications. "The main benefit of AI is that it can bridge the gap between humans and technology," says AI researcher Robb Wilson.

Now senior director for AI and legal ethics at NVIDIA, Pope spends her days working with internal teams across the company to ensure its products engender trust across industries. In a recent "Solving for Tech Ethics" podcast, Pope joined Beena Ammanath, Deloitte LLP's Trustworthy and Ethical Technology leader, to discuss the challenges and opportunities associated with creating trustworthy AI. Ammanath: Five or 10 years ago, roles like yours just didn't exist. What does a day in your job look like? Pope: One day does not look like the next. Take yesterday as an example.

"As humans, we are highly biased," says Beena Ammanath, the global head of the Deloitte AI Institute, and tech and AI ethics lead at Deloitte. "And as these biases get baked into the systems, there is very high likelihood of sections of society being left behind--underrepresented minorities, people who don't have access to certain tools--and it can drive more inequity in the world." Projects that begin with good intentions -- to create equal outcomes or mitigate past inequities -- can still end up biased if systems are trained with biased data or researchers aren't accounting for how their own perspectives affect lines of research. Thus far, adjusting for AI biases has often been reactive with the discovery of biased algorithms or underrepresented demographics emerging after the fact, says Ammanath. But, companies now have to learn how to be proactive, to mitigate these issues early on, and to take accountability for missteps in their AI endeavors.

"You won't see many people with my background talking about ethics," said Beena Ammanath, executive director of the Global Deloitte AI Institute and head of Trustworthy AI and Ethical Tech at the global consulting company. A computer scientist who worked as a database and SQL developer and held data science- and AI-related technology roles at Bank of America, GE and Hewlett Packard before joining Deloitte in 2019, Ammanath wasn't always gung-ho to talk AI ethics. Then she decided to write a book about it. "There has arguably never been a more exciting time in AI," she wrote in her book "Trustworthy AI." "Alongside the arrival of so much promise and potential, however, the attention placed on AI ethics has been relatively slight." Protocol spoke with Ammanath about why ethical AI practices should be part of every employee's training, the limitations of providing internal guidance inside a sprawling consultancy and why she finally gave in and joined the AI ethics conversation.