Microsoft has appointed the co-founder of the British artificial intelligence lab DeepMind as the head of a new AI division. Mustafa Suleyman, 39, co-founded DeepMind with Demis Hassabis and Shane Legg in 2010 and the company went on to be bought by Google for 400m in 2014. It now forms the core of Google's AI efforts after merging with another unit to become Google DeepMind in 2023. The chief executive of Microsoft, Satya Nadella, announced in a blogpost that the British AI pioneer, who left DeepMind in 2019, will be chief executive of a new organisation called Microsoft AI focusing on the US company's consumer products and research. Several employees at Sulyeman's Inflection AI startup will join the division.

The White House has met with AI executives, released an AI bill of rights and an AI risk management framework, but who should run the show? WASHINGTON, D.C. – Congressional lawmakers agreed that AI needs federal oversight, but several were skeptical that President Biden or Vice President Kamala Harris were capable of leading the effort. "I wouldn't trust Joe Biden and Kamala Harris to be able to successfully operate an iPhone, much less be a key focal point of AI policy," Florida Rep. Matt Gaetz told Fox News. "That said, there are some leading minds in the Democratic Party here on the Hill who I think are evaluating these issues with great thoughtfulness: Ted Lieu, Ro Khanna." Rep. Matt Gaetz said neither Biden nor Harris should run the White House's AI efforts.

There are many excellent books and articles describing those topics and how they can be implemented in various software frameworks, and those descriptions will not be repeated here. There also are many articles on Big Tech implementing AI at scale. But how do "regular" organizations implement AI projects successfully, especially within an existing portfolio of solutions? In the BLOG@CACM post "Anna Karenina on Development Methodologies," I described how the famous opening line "happy families are all alike, unhappy families are unhappy each in their own way" applies to software development. This post will describe in a similar vein the development behaviors with the highest chance of success for AI efforts.

Mark Zuckerberg introduced Facebook's rebranding to Meta at the company's annual Connect event last year to reposition the company for the "new internet," the metaverse. The metaverse has been around for some time as a kind of urban legend, perhaps aptly described in the 2011 science fantasy book Ready Player One. That was until some of the biggest names in tech started investing heavily in related technologies, including virtual and augmented reality (VR/AR), Internet of Things (IoT), and artificial intelligence (AI). Today AI is one of the most exciting technology fields to work on. Zuckerberg said the metaverse is something he's wanted to work on since even before the conception of Facebook.

The effectiveness of AI efforts can be quantifiably increased using tried-and-true MLops methodologies in terms of time to market, results, and long-term sustainability. The long-term success of AI projects depends on effectively closing that operational capability gap because building models that make accurate predictions are only a small portion of the entire task. There is more to creating ML systems that add value to a company. An efficient technique calls for regular iteration cycles with ongoing monitoring, care, and improvement, as opposed to the ship-and-forget pattern typical of traditional software. Enter MLops (machine learning operations), which enables teams from the IT operations, engineering, and data science departments to collaborate to deploy ML models into production, manage them at scale, and continuously track their performance. MLops typically aims to address six critical challenges around taking AI applications into production.

Transparency often plays a key role in ethical business dilemmas -- the more information we have, the easier it is to determine what are acceptable and unacceptable outcomes. If financials are misaligned, who made an accounting error? If data is breached, who was responsible for securing it and were they acting properly? But what happens when we look for a clear source of an error or problem and there's no human to be found? That's where artificial intelligence presents unique ethical considerations. AI shows enormous potential within organizations, but it's still largely a solution that is looking for a problem.

Craig Martell, Head of Machine Learning at Lyft, is set to head the Pentagon's AI efforts. Breaking Defense first broke the news after learning Martell was destined to be named as the Pentagon's new chief digital and AI officer. Martell has significant AI industry experience – leading efforts at not just Lyft but also Dropbox and LinkedIn – but has no experience navigating public-sector bureaucracy. The Pentagon is going to be very much "in at the deep-end" for Martell in that regard, something which he fully acknowledges. "I don't know my ways around the Pentagon yet and I don't know what levers to pull," said Martell to Breaking Defense.

The last year was a busy one for Russia's military and civilian artificial intelligence efforts. Moscow poured money into research and development, and Russia's civil society debated the country's place in the larger AI ecosystem. But Vladimir Putin's invasion of Ukraine in February and the resulting sanctions have brought several of those efforts to a halt--and thrown into question just how many of its AI advancements Russia will be able to salvage and continue. Ever since Putin extolled the development of robotic combat systems in the new State Armaments Program in 2020, the Russian Ministry of Defense has been hyper-focused on AI. We have learned more about the Russian military's focus on AI in the past year thanks to several public revelations.

Artificial intelligence (AI) has become part of our daily life, whether through search engine algorithms or the use of Siri or Alexa on our mobile phones for translation, taxi requests or maths calculations. Businesses are taking big steps in adopting AI in their daily operations. The percentage of enterprises employing AI has grown by 270 percent over the past few years, according to Gartner, Inc. Total AI investment by firms globally surged to a record-high $77.5 billion in 2021, up from $36 billion in 2020. Its key findings reveal that the US has benefited most from this investment drive: US-based companies pulled in two-thirds of total global venture funding in AI in 2021 – or $51 billion worth of investment.

Even though AI continues to thrive and grow, there remain challenges to use the technology. Just some include finding data scientists, determining the right problems to focus on, getting quality data and scaling the models. No doubt, these problems are even worse for non-tech companies. They generally do not have the expertise or sufficient resources to make AI a success. "Research shows non-tech companies in particular have struggled to take their AI programs beyond the proof of concept and pilot phases–with just 21% of retail, 17% of automotive, 6% of manufacturing, and 3% of energy companies successfully scaling their AI use cases," said Jerry Kurtz, who is the Executive Vice President of Insights and Data at Capgemini North America.