We only need to look toward prominent Industry 4.0 models to see how artificial intelligence (AI) is impacting the future of manufacturing. It's baked into the roadmap and is arguably a vital part of the end state. Artificial intelligence can ingest a combination of data from sensors, machines, and people and then apply it to algorithms designed to optimize operations or achieve lights out manufacturing. Back in reality, we have some time before leading organizations achieve this pinnacle state of manufacturing, let alone the industry as a whole. Only about 9% of manufacturing organizations are leveraging Artificial Intelligence today.

The Pentagon's Joint Artificial Intelligence Center is recruiting businesses to help prepare military data for use with AI. The solicitation released March 31 is a sign of the AI office's shifting role from product developer to provider of AI readiness services for Defense Department components. The basic ordering agreement would allow those components and federal partners to issue task orders for the work to get data in shape for artificial intelligence -- that could include everything from capturing data to sorting it for storage to modeling how employees will use it with AI to get better insights. The Data Readiness for Artificial Intelligence Development (DRAID) Services ordering agreement will "help the DoD and Government users prepare data for use in AI applications by providing an easily accessible path to access the cutting-edge commercial services needed to meet the complex technical challenges involved in preparing data for AI," the solicitation read. "The services addressed by the DRAID span the entire AI data preparation lifecycle, from data ingestion, through labeling, right up to before model training begins," an April 1 blog post from the JAIC states.

The government's highest priority investment in artificial intelligence needs to be its AI workforce. It is not adopting AI as quickly as the private sector, and potentially as quickly as our adversaries. Most government teams developing AI solutions we have met face high barriers when they begin a project. They include limited access to data sets, constrained system authorities, and less computing power than they need. As a result, projects are slower and more expensive than they might be, delaying the fielding of systems that can decrease costs, increase capabilities, and help improve national security.An educated, trained, and empowered AI workforce can act as a catalyst, enabling the government to create and adopt AI capabilities far more quickly and effectively than it does now.

The government's highest priority investment in artificial intelligence needs to be its AI workforce. It is not adopting AI as quickly as the private sector, and potentially as quickly as our adversaries. Most government teams developing AI solutions we have met face high barriers when they begin a project. They include limited access to data sets, constrained system authorities, and less computing power than they need. As a result, projects are slower and more expensive than they might be, delaying the fielding of systems that can decrease costs, increase capabilities, and help improve national security.

As an international relations scholar who studies space law and policy, I have come to realize what most people do not fully appreciate: Dealing with space debris is as much a national security issue as it is a technical one. Considering the debris circling the Earth as just an obstacle in the path of human missions is naive. As outer space activities are deeply rooted in the geopolitics down on Earth, the hidden challenge posed by the debris is the militarization of space technologies meant to clean it up. To be clear, space debris poses considerable risks; however, to understand those risks, I should explain what it is and how it is formed. As outer space activities are deeply rooted in the geopolitics down on Earth, the hidden challenge posed by the debris is the militarization of space technologies meant to clean it up.

The wave of AI and Machine Learning is happening just as the dominance of mobile is becoming set in stone. As mobile devices become more ubiquitous and powerful, a lot of the ML tasks we think of as requiring months of high powered compute time will be able to happen right on your phone. This post will outline why edge devices are increasingly important, and how Machine Learning works with them. Unsurprisingly, this trend is starting to impact Machine Learning as well. A lot of the cool new features that we love on iPhone -- like face recognition in photos and FaceID -- rely on ML, some of which takes place on device.

Machine Learning is emerging as a serious technology just as mobile is becoming the default method of consumption, and that's leading to some interesting possibilities. Smartphones are packing more power by the year, and some are even overtaking desktop computers in speed and reliability. That means that a lot of the Machine Learning workloads that we think of as requiring specialized, high priced hardware will soon be doable on mobile devices. This post will outline this shift and how Machine Learning can work with the new paradigm. Unsurprisingly, this trend is starting to impact Machine Learning as well.