In some cases, these AI advisors have become a standard part of some firms' project delivery methods. But it's still a challenge to convince construction professionals to listen to these AI advisors, and there are emerging questions of how risk will be allocated once algorithm-driven decisions start to steer projects. One of the more direct uses of AI in construction has been the project scheduling analysis performed by ALICE Technologies' machine-learning algorithm, ALICE. The company has made inroads into the industry in recent years (ENR 5/28/18 p.22), but founder René Morkos says that construction may be approaching a tipping point when it comes to AI adoption. "What I always hear from people [in the industry] is that'I really like scheduling, but the number crunching is the boring part,'" says Morkos.

Interpretability in machine learning (ML) is crucial for high stakes decisions and troubleshooting. In this work, we provide fundamental principles for interpretable ML, and dispel common misunderstandings that dilute the importance of this crucial topic. We also identify 10 technical challenge areas in interpretable machine learning and provide history and background on each problem. Some of these problems are classically important, and some are recent problems that have arisen in the last few years. These problems are: (1) Optimizing sparse logical models such as decision trees; (2) Optimization of scoring systems; (3) Placing constraints into generalized additive models to encourage sparsity and better interpretability; (4) Modern case-based reasoning, including neural networks and matching for causal inference; (5) Complete supervised disentanglement of neural networks; (6) Complete or even partial unsupervised disentanglement of neural networks; (7) Dimensionality reduction for data visualization; (8) Machine learning models that can incorporate physics and other generative or causal constraints; (9) Characterization of the "Rashomon set" of good models; and (10) Interpretable reinforcement learning. This survey is suitable as a starting point for statisticians and computer scientists interested in working in interpretable machine learning.

Recent study by Dr Kristina Irion joins up three EU policy areas that intersect in the digital age: consumer protection, EU governance of AI and EU external trade. "They are becoming so intertwined', says Dr. Irion. 'My research is breaking up the silos and drawing insights from their interactions.' According to the research, the source code clause within trade law restricts the EU's right to regulate AI policy. Dr Irion's study concludes that the EU position on source code in international trade agreements limits the EU's ability to regulate AI in the interests of consumers.

AI and machine learning have been hot buzzwords in 2020. As we approach 2021, it's a good time to take a look at five "big-picture" trends and issues around the growing use of artificial intelligence and machine learning technologies. Hyperautomation, an IT mega-trend identified by market research firm Gartner, is the idea that most anything within an organization that can be automated -- such as legacy business processes -- should be automated. The pandemic has accelerated adoption of the concept, which is also known as "digital process automation" and "intelligent process automation." AI and machine learning are key components -- and major drivers -- of hyperautomation (along with other technologies like robot process automation tools).

Research papers come out far too rapidly for anyone to read them all, especially in the field of machine learning, which now affects (and produces papers in) practically every industry and company. This column aims to collect some of the most relevant recent discoveries and papers -- particularly in but not limited to artificial intelligence -- and explain why they matter. This week brings a few unusual applications of or developments in machine learning, as well as a particularly unusual rejection of the method for pandemic-related analysis. One hardly expects to find machine learning in the domain of government regulation, if only because one assumes federal regulators are hopelessly behind the times when it comes to this sort of thing. So it may surprise you that the U.S. Environmental Protection Agency has partnered with researchers at Stanford to algorithmically root out violators of environmental rules.

When COVID-19 hit, organizations using traditional analytics techniques that rely heavily on large amounts of historical data realized one important thing: Many of these models are no longer relevant. Essentially, the pandemic changed everything, rendering a lot of data useless. In turn, forward-looking data and analytics teams are pivoting from traditional AI techniques relying on "big" data to a class of analytics that requires less, or "small" and more varied. Transitioning from big data to small and wide data is one of the Gartner top data and analytics trends for 2021. These trends represent business, market and technology dynamics that data and analytics leaders cannot afford to ignore.

Over-consumption of the news can be detrimental to your mental, physical and emotional well being. Veuer’s Chloe Hurst has the story!. See more videos about Videos, Race & Ethnicity, Diversity, Racism, Natural History, Museums.

We all use facial recognition to unlock our phones. And we all view online content automatically suggested to us. But some of us have rather more success with artificial intelligence (AI) than others. A study of face recognition AIs discovered that systems from leading companies IBM, Microsoft and Amazon misclassified the faces of Oprah Winfrey, Michelle Obama and Serena Williams, while having no trouble at all with white males. Even the voices of digital assistants such as Cortana or Google Assistant have female voices by default, perhaps unconsciously reinforcing the stereotype of female subservience in the minds of millions of users.

In her first major speech to a U.S. audience after the U.S. presidential election, European Commission President Ursula von der Leyen laid out priority areas for transatlantic cooperation. She proposed building a new relationship between Europe and the United States, one that would encompass transatlantic coordination on digital technology issues, including working together on global standards for regulating artificial intelligence (AI) aligned with EU values. A reference to cooperation on standards for AI was included in the New Transatlantic Agenda for Global Change issued by the Commission on December 2, 2020. In remarks to Parliament on January 22, 2021, President von der Leyen called for "creating a digital economy rule book" with the United States that is "valid worldwide." Some would say Europe's new outreach on issues of tech governance and the suggestion of establishing an "EU-U.S. Trade and Technology Council" is incongruous to the current regulatory war being waged against ...

"I created this solution based on what I personally wish I had access to," says Ritika Dutt, co-founder and CEO of Botler AI, a system helping sexual misconduct victims receive resources specific to their situation.