Last month, Microsoft launched their new tool, “Productivity Score”. According to Microsoft, this new tool will help organizations understand how well they are functioning, how technology affects their productivity, and how they can get the most out of their Microsoft 365 purchase.

But to do all of this, the tool will keep track of how each employee is using Microsoft products. For instance, the tool will monitor how often video or screen sharing is enabled during meetings by employees.

It will keep a metric of how employees are communicating. It will show if employees are sending out emails through Outlook, sending out messages through Teams, or posting on Yammer. It will also keep track of which Microsoft tools are being used more and on which platforms.

So, Microsoft’s new tool is a scary work surveillance tool, right? According to Microsoft, it isn’t. In a blog post, Microsoft 365’s corporate Vice President Jared Spataro said, “Productivity Score is not a work monitoring tool. Productivity Score is about discovering new ways of working, providing your people with great collaboration, and technology experiences.”

Spataro says the tool “focuses on actionable insights” so people and teams can use Office 365 tools to be more productive, collaborative, and help make work improvements. And, while this all sounds good, privacy advocates aren’t too thrilled about this.

Microsoft says it is “committed to privacy as a fundamental element of Productivity Score.” To maintain privacy and trust, the tool does aggregate user data over a 28-day period. And, there are controls to anonymize user information, or completely remove it. However, by default individual-level monitoring is always on, and only admins can make any of these changes. Employees can’t do anything about securing their privacy.

So, user data privacy is still a large issue on the table, but privacy advocates can breathe a sigh of relief. Yesterday, they got a response from Microsoft they can smile about. In another blog post, Spataro responded to the controversy. “No one in the organization will be able to use Productivity Score to access data about how an individual user is using apps and services in Microsoft 365,” he said.

Although Productivity Score will still aggregate data over a 28-day period, it will not do so from an individual employee level. It will do it from an organizational one as a whole. Also, the company is making it clearer that the tool is a “measure of organizational adoption of technology—and not individual user behavior.”

Big companies scout like for small ones that have proven ideas and prototypes, rather than take the initial risk on themselves. So startups have to stay ahead of technology by their very nature, in order to be stand-out candidates when selling their ideas to investors.

Innovation Leader, in partnership with KPMG LLP, recently conducted a study that sheds light onto the bleeding edge of tech: The technologies that the biggest companies are most interested in building right now.

The study asked its respondents to group 16 technologies into four categorical buckets, which Innovation Leader CEO Scott Kirsner refers to as “commitment level.”

The highest commitment level, “in-market or accelerating investment,” basically means that technology is already mainstream. For optimum tech-clairvoyance, keep your eyes on the technologies which land in the middle of the ranking.

“Investing or piloting” represents the second-highest commitment level – that means they have offerings that are approaching market-readiness.

The standout in this category is Advanced Analytics. That’s a pretty vague title, but it generally refers to the automated interpretation and prediction on data sets, and has overlap with Machine learning.

Wearables, on the other hand, are self explanatory. From smart watches to location trackers for children, these devices often pick up on input from the body, such heart rate.

The “Internet of Things” is finding new and improved ways to embed sensor and network capabilities into objects within the home, the workplace, and the world at large. (Hopefully that doesn’t mean anyone’s out there trying to reinvent Juicero, though.)

Collaboration tools and cloud computing also land on this list. That’s no shock, given the continuous pandemic.

The next tier is “learning and exploring”— that represents lower commitment, but a high level of curiosity. These technologies will take a longer time to become common, but only because they have an abundance of unexplored potential.

Blockchain was the highest ranked under this category. Not surprising, considering it’s the OG of making people go “wait, what?”

Augmented & virtual reality has been hyped up particularly hard recently and is in high demand (again, due to the pandemic forcing us to seek new ways to interact without human contact.)

And notably, AI & machine learning appears on rankings for both second and third commitment levels, indicating it’s possibly in transition between these categories.

The lowest level is “not exploring or investing,” which represents little to no interest.

Quantum computing is the standout selection for this category of technology. But there’s reason to believe that it, too, is just waiting for the right breakthroughs to happen.

The deep learning that modifies your social media and gives you Google search results is coming to your thermostat.

Researchers at the Massachusetts Institute of Technology (MIT) have developed a deep learning system of neural networks that can be used in the “Internet of Things” (IoT). Named MCUNet, the system designs small neural networks that allow for previously unseen speed and accuracy for deep learning on IoT devices. Benefits of the system include energy savings and improved data security for devices.

Created in the early 1980s, the IoT is essentially a large group of everyday household objects that have become increasingly connected through the internet. They include smart fridges, wearable heart monitors, thermostats, and other “smart” devices. These gadgets run on microcontrollers, or computer chips with no processing system, that have very little processing power and memory. This has traditionally made it hard for deep learning to occur on IoT devices.

“How do we deploy neural nets directly on these tiny devices? It’s a new research area that’s getting very hot,” said Song Han, Assistant Professor of Computer Science at MIT who is a part of the project, “Companies like Google and ARM are all working in this direction.”

In order to achieve deep learning for IoT connected machines, Han’s group designed two specific components. The first is TinyEngine, an inference engine that directs resource management similar to an operating system would. The other is Tiny NAS, a neural architecture search algorithm. For those not well-versed in such technical terms, think of these things like a mini Windows 10 and machine learning for that smart fridge you own.

The results of these new components are promising. According to Han, MCUNet could become the new industry standard, stating that “It has huge potential.” He envisions the system has one that could help smartwatches not just monitor heartbeat and blood pressure but help analyze and explain to users what that means. It could also lead to making IoT devices far more secure than they are currently.

“A key advantage is preserving privacy,” says Han. “You don’t need to transmit the data to the cloud.”

It will still be a while until we see smart devices with deep learning capabilities, but it is all but inevitable at this point—the future we’ve all heard about is definitely on the horizon.