Google has announced that it will eliminate at least 17 features from its Assistant product, following news that it had laid off "hundreds" of employees from the division. The company is cutting "underutilized features" to "focus on quality and reliability, it wrote in a blog post, even though a good number of people may still rely on those functions. "Beginning on January 26, when you ask for one of these features, you may get a notification that it won't be available after a certain date," wrote Google Assistant VP Duke Dukellis. The company didn't specify how removing certain commands will improve Assistant, nor did it describe any specific quality and reliability problems. It did say, though, that improvements in the past were aided by user feedback, so it may have been receiving complaints about Assistant's core usability of late.

With automation becoming increasingly popular in the field of machine learning, one may wonder if the role of humans in machine learning will become non-essential at some point. When building a machine learning model, it's important to remember that the model must produce meaningful and interpretable results in real-life situations. This is where the human experience comes in. A human (qualified data science professional) has to examine the results produced by algorithms and computers to ensure that the results are consistent with real-world situations before recommending a model for deployment. With automation in machine learning, humans are still indispensable to make the connection between data, algorithms, and the real world.

There seems to be a tendency to hastily use imperfect and questionable data to train an AI solution for COVID-19, a dangerous trend that not only does not help any patient or physician but also damages the reputation of the AI community. Dealing with a pandemic -- as significant as it is -- does not suspend basic scientific principles. Data has to be curated by medical experts, full and rigorous validations have to be performed, and results have to be reviewed by peers before we deploy any solution or even proposal into the world, particularly when society is dealing with many uncertainties. It is safe to say we are all deeply concerned about the COVID-19 pandemic. This coronavirus has drastically changed our reality: We're experiencing stress, restrictions, quarantines; we're witnessing heroic sacrifices of caregivers including staff, nurses, and physicians; we're losing loved ones; and we're facing economic hardships and massive uncertainties about what is in store in the coming months.

What exactly is artificial intelligence (AI) and what business does it have in higher education? Simply put, AI is an attempt to emulate human knowledge by programming extensive rules into computers. Through machine learning and expert systems, machines can produce patterns within mass flows of data and pinpoint correlations that couldn't possibly be immediately intuitive to humans. The developmental capabilities and precision of AI ultimately depend on the gathering of data – Big Data. Where better to find a continuous stream of information than within the highly active and engaging community of students.

Artificial Intelligence (AI) systems are taking over a vast array of tasks that previously depended on human expertise and judgment. Often, however, the "reasoning" behind their actions is unclear, and can produce surprising errors or reinforce biased processes. One way to address this issue is to make AI "explainable" to humans--for example, designers who can improve it or let users better know when to trust it. Although the best styles of explanation for different purposes are still being studied, they will profoundly shape how future AI is used. Some explainable AI, or XAI, has long been familiar, as part of online recommender systems: book purchasers or movie viewers see suggestions for additional selections described as having certain similar attributes, or being chosen by similar users.

Organizational leaders have widely recognized the promise of AI as the cornerstone of digital transformation and it's no surprise that many are now attempting to accelerate its deployment and adoption. However, most of these same organizations are still struggling to increase adoption and interest in analytics. Even with the emergence of business intelligence (BI) platforms, promises of better decision-making can go unfulfilled without widespread adoption. For an organization to have any chance at success with AI, it must first have a solid BI strategy rooted in the core pillars of people, process, and platform. In recent years, many organizations have moved beyond basic descriptive analytics and into more diagnostic analysis, but few have created a true self-service environment capable of embracing the benefits and risks of AI.