Rama Ramakrishnan is a professor of the practice at MIT Sloan. He specializes in data science and machine learning. He was a data science entrepreneur and tech executive for more than 20 years, most recently as senior vice president at Salesforce and chief data scientist for Salesforce Commerce Cloud. An increasing number of organizations are bringing data scientists on board as executives and managers recognize the potential of data science and artificial intelligence to boost performance. But hiring talented data scientists is one thing; harnessing their capabilities for the benefit of the organization is another.

Optimization problems: this is still a bit of an unexplored and immature domain, with little (user-friendly) tooling available, like I also mentioned in one of my previous blogs. Interesting names to look at are JuMP (based on Julia language), ADMB, GLPK, OpenMDAO, Motulus, OptaPlanner… However all those tools are still rather complex and therefore still difficult to use for non-specialized developers.

Miao Chen, a data scientist at TEL Singapore, describes how analytics plays a key role in helping organizations develop and maintain their competitive advantage – and how automation has become increasingly important to many industries. Chen talks about automation as a promising way for a variety of functional teams in an organization to overcome the complexity of data sources, ease the shortage of right resources and accelerate the process of extracting value from data. This presentation provides some basic background on automation in analytics, as well as the benefits of and techniques for automating analyses using JMP scripts.

The need to make decisions with sufficient quality is only compatible in some cases with the data we have at hand to reach this goal. If we want to advance drug discovery, then acknowledging the suitability of a given end point to answer a given question is at least as important as modelling a particular end point. . . The problem is, modeling is easier to start doing than dealing with that suitability question. It can also be harder to explain this point to investors, to granting agencies, and to upper management, because improvements in things like assay quality and target selection are harder to quantify and come on slowly. This, to me, is the big question looming over a lot of AI/ML approaches to drug discovery, and I'm really glad to see a paper addressing it head-on.

As the field of robotics matures, our community must grapple with the multifaceted impact of our research; in this article, we describe two previous workshops hosting robotics debates and advocate for formal debates to become an integral, standalone part of major international conferences, whether as a plenary session or as a parallel conference track. As roboticists build increasingly complex systems for applications spanning manufacturing, personal assistive technologies, transportation and others, we face not only technical challenges, but also the need to critically assess how our work can advance societal good. Our rapidly growing and uniquely multidisciplinary field naturally cultivates diverse perspectives, and informal dialogues about our impact, ethical responsibilities, and technologies. Indeed, such discussions have become a cornerstone of the conference experience, but there has been relatively little formal programming in this direction at major technical conferences like the IEEE International Conference on Robotics and Automation (ICRA) and Robotics: Science and Systems (RSS) Conference. To fill this void, we organized two workshops entitled "Debates on the Future of Robotics Research" at ICRA 2019 and 2020, inspired by a similar workshop at the 2018 International Conference on Machine Learning (ICML).

Over the last few years, business leaders have invested millions into setting up AI/data science teams to gain a competitive advantage. Some AI initiatives have resulted in measurable benefits, but many haven't. Initial exuberance among business leaders has given way to skepticism. In our experience collaborating with Fortune 500 enterprises at TheMathCompany, we've observed that despite having sophisticated AI tools and star-studded data science teams on their side, weak links in strategic and operational aspects have deprived organizations of deriving meaningful value from AI. Based on our work across industry verticals helping organizations go through analytical transformations, I recommend five best practices to ensure tangible and sustainable value from AI.

Scientific advances over the last several centuries have not only resulted in a greater understanding of the universe; they've raised the standard of living for many people across the globe. However, there are still massive challenges we're ill equipped to meet, as evidenced by climate change and the COVID-19 pandemic, which have shown that we are yet to understand the complexity of nature. In order to address the scale of problems now facing humanity, radical solutions are needed--and scientific breakthroughs will be central to this process. Artificial intelligence promises to accelerate fundamental discoveries by deepening the nature of questions researchers can ask. In his visionary essay "As We May Think," published in 1945, the prominent American engineer and science advocate Vannevar Bush predicted that people would soon need to rely on external devices to augment their minds.

Over the last several years, there's been a rush to find out how to integrate AI into businesses, and it's no secret that doing so could offer huge comparative advantages. But for all the hype, AI in businesses is still very much in the early phase. Our team hails from Uber, Google, Facebook and Adobe where we've seen both the positives and challenges of deploying AI across business lines. Most companies don't have the same resources to build in-house tools, deeply measure results and fund extensive research. Our goal with this blog is to use our in-depth knowledge of the AI space to make sense of the ecosystem, cut through the hype, and provide insights that can help you with your AI investment decisions across the pipeline.

There are numerous possibilities for increasing the effectiveness of an organization's functions. There appears to be high levels for duplication of effort – people may not know where in a process a particular item is. 5. An expert has difficulty in quantifying his or her knowledge – a variety of experts may be needed in the same area. Although some of these applications entail carefully walking through the thickets of internal politics, the rewards can justify the effort. Whatever the parameters used in selecting an application, the bottom-line questions should always be asked. What will be the impact on the business?

If the application requirements can be met with the use of conventional computing techniques, they should be used because it is probably easier to use conventional methods than the technology of artificial intelligence. However, if it is difficult to define quantitatively and in straightforward serial logic the permutations and approaches to solving the problem, then AI technology is likely to be the better choice. In most cases – and this is where the evaluation of opportunities can be critical. Some have suggested that AI projects focus on high-value applications. "Pick a problem whose solution could save the organization a couple of million dollars a year."