Artificial intelligence startup Domino Data Lab Inc. said Tuesday it raised $100 million in new funding amid increased business interest in tools that help data scientists build and deploy AI applications. The funding will be used to scale its sales organization and build out its machine-learning platform's features and functions, said Nick Elprin, Domino Data's chief executive and one of its co-founders. Domino Data has raised $228 million since its founding in 2013. Private-equity firm Great Hill Partners led the series F round with participation from graphics-chip maker Nvidia Corp. and existing investors Coatue Management, Highland Capital Partners and Sequoia Capital. The company didn't disclose its valuation.

Model Predictive Control (MPC) is a versatile and a widely used for model-based control approaches, which involves an online optimization of the control strategy over a pre-determined predictive receding horizon. A central limitation of the traditional MPC online optimization is that it requires a relatively inexpensive models. As a result, linear and non-linear (quadratic) approximations to the plant-models are considered - unless, of-course an explicit model in the form of a differential equation is readily available. The non-linear modeling presents a computation challenge, that requires one to solve nonlinear programming problems online. This works fine for relatively low-dimensional systems.

Industrial companies are embracing artificial intelligence (AI) as part of the fourth digital revolution. 1 1. The first two revolutions introduced programmable logic controllers and distributed control systems, which enabled plant-wide data collection and automation. The third revolution--advanced process controls--further abstracted automation into high-level models, allowing for increasingly dynamic plant operation. For more on the latest innovations in process controls, see Stephan Görner, Andy Luse, Naman Maheshwari, Ravi Malladi, Lapo Mori, and Robert Samek, "The potential of advanced process controls in energy and materials," November 23, 2020. AI leverages big data; it promises new insights that derive from applying machine learning to datasets with more variables, longer timescales, and higher granularity than ever.

CEOs often live by the numbers--profit, earnings before interest and taxes, shareholder returns. These data often serve as hard evidence of CEO success or failure, but they're certainly not the only measures. Among the softer, but equally important, success factors: making sound decisions that not only lead to the creation of value but also "do no harm." While artificial intelligence (AI) is quickly becoming a new tool in the CEO tool belt to drive revenues and profitability, it has also become clear that deploying AI requires careful management to prevent unintentional but significant damage, not only to brand reputation but, more important, to workers, individuals, and society as a whole. Legions of businesses, governments, and nonprofits are starting to cash in on the value AI can deliver.

Artificial intelligence (AI) has been mainly the passion of data science labs and development shops. Lately, however, the implications of its potential impact on business -- in the form of enhanced customer service, expanded intelligent capabilities, and even society at large -- have become clearer. That means the time has come for business leaders to not only understand the implications of AI, but also step up and lead the way. That's because with the great power of AI comes great responsibility. "While AI is quickly becoming a new tool in the CEO tool belt to drive revenues and profitability, it has also become clear that deploying AI requires careful management to prevent unintentional but significant damage, not only to brand reputation but, more important, to workers, individuals, and society as a whole," write Roger Burkhardt, Nicolas Hohn, and Chris Wigley, all with McKinsey.

Here's the conundrum with corporate online learning: there are so many classes available from sites like Coursera, edX, and Udacity that companies don't know what content to offer their employees. And once companies do choose a learning program, it's tough for them to figure out what skills their employees pick up and to what degree they've mastered them. They need an objective metric to evaluate proficiency. A new AI-powered tool developed by Coursera aims to be that metric. The feature, which the Bay Area startup announced today, lets companies that subscribe to its training programs see which of their employees are earning top scores in Coursera classes; how their employees' skills measure up to their competitors'; and what courses would help fill any knowledge gaps.

For example, Bates said a founder has to decide if they're going to hire a data-science team to run the project, have existing engineers work on it in their spare time or outsource the project to an outside data-science team. While using existing engineers might be the most attractive and affordable option, Bates warns against that option, as it runs the possibility of your engineers not having the background to fully understand the algorithms and issues that come up. As to deciding between building a team in-house or outsourcing, one thing to consider is if the project could exist without machine learning. If it's crucial to the identity of the project, it's worthwhile to invest in building a dedicated in-house team.