Data Science, Artificial Intelligence, Analytics, and Machine Learning at the Enterprise scale are terms you've probably heard before. But what do they mean? We break it down for you in this blog. So, What Is Data Science? Data Science is a series of disciplines, technology, skills, expertise, and knowledge that encompass one thing: obtaining and preparing data for analysis.

Sephus, applied science manager for Amazon Artificial Intelligence, focuses on fairness and the detection of biases in data and machine learning …

Products we use every day now have become increasingly software driven, connected and sophisticated. So your engineering process has become exponentially more complex. Consider the automobile, for example. Today's automobiles require millions of lines of software to operate critical systems like braking, engine performance and collision avoidance. The implementation of more requirements, more dependency on modelling, greater testing as well as increasing global collaboration between teams to cope with customer demands have created new challenges.

The year 2019 seemed to be the year of unpredictability, not the least of which was the seemingly ever-changing foreign trade policy of major world economies. Interestingly, it's that same unpredictable nature of foreign trade policy that serves as a springboard for supply chain predictions for 2020. Here are the top five predictions that will have a major impact on the world's global supply chains. Historically, digital transformation of the supply chain has taken place by targeting various functional silos within their own walls. This approach lacked the ability to evaluate the interconnected nature of supply chain decisions.

Australian IBM cybersecurity engineers have developed an artificial intelligence (AI) system to analyse network connections and employee communications at an enterprise scale. The model detects changes in users' behaviour and can automatically triggers investigations even if the changes occur across multiple platforms. IBM research found the root cause for 52 per cent of data breaches in Australia was malicious or criminal attacks which often use methods like phishing and social engineering. The new IBM solution, developed in the company's Gold Coast cybersecurity lab as part of a hackathon, uses AI to monitor changes in employee behaviour and flags indicators of compromise. It was debuted to the industry at last week's Australian Cyber Conference in Melbourne as a way of showing what can be done but the solution is not something that can be bought directly from IBM. Currently known as "QRadar Insider Threat Detector with Watson" it uses IBM's AI model, Watson, to analyse user generated content – like emails, Word documents, and Slack messages – to detect both the tone of content and employees' typical behaviour or "personalities".

According to a McKinsey Global Survey, approximately 30% of executives reported active pilot projects, while 71% were expecting a significant increase in AI investment. However, the survey found that progress remained slow, most companies didn't have a clear strategy or infrastructure for sourcing data, and organizations were lacking the foundational building blocks to create value from AI at scale. Deploying AI in industrial operations is difficult for a variety of reasons – complex data management, challenging integration, enterprise security requirements, real-time analytics and capability to handle thousands of models in the production environment. However, a fundamental problem is finding skilled people to implement AI. To circumvent this issue, companies are relying on citizen data scientists – subject matter experts with domain expertise in operations – and providing them with advanced analytical tools.

According to a McKinsey Global Survey, approximately 30% of executives reported active pilot projects, while 71% were expecting a significant increase in AI investment. However, the survey found that progress remained slow, most companies didn't have a clear strategy or infrastructure for sourcing data, and organizations were lacking the foundational building blocks to create value from AI at scale. Deploying AI in industrial operations is difficult for a variety of reasons – complex data management, challenging integration, enterprise security requirements, real-time analytics and capability to handle thousands of models in the production environment. However, a fundamental problem is finding skilled people to implement AI. To circumvent this issue, companies are relying on citizen data scientists – subject matter experts with domain expertise in operations – and providing them with advanced analytical tools.

The very large majority of open-source MT efforts fail because they do not consistently produce output that is equal to, or better than, any easily accessed public MT solution or because they cannot be deployed effectively. This is not to say that this is not possible, but the investments and long-term commitment required for success are often underestimated or simply not properly understood. A case can always be made for private systems that offer greater control and security, even if they are generally less accurate than public MT options. However, in the localization industry we see that if "free" MT solutions that are superior to an LSP-built system are available, translators will use them. We also find that for the few self-developed MT systems that do produce useful output quality, integration issues are often an impediment to deployment at enterprise scale and robustness. Some say that those who ignore the lessons of history are doomed to repeat errors.

We live in a time when there is a proliferation of open-source machine learning and AI-related development platforms. Thus, people believe that given a large amount of data and a few computers, a functional and useful MT system can be developed with a do-it-yourself (DIY) tool kit. However, as many who have tried have found out, the reality is much more complicated, and the path to success is long, winding and sometimes even treacherous. The very large majority of open-source MT efforts fail because they do not consistently produce output that is equal to, or better than, any easily accessed public MT solution or because they cannot be deployed effectively. This is not to say that this is not possible, but the investments and long-term commitment required for success are often underestimated or simply not properly understood. A case can always be made for private systems that offer greater control and security, even if they are generally less accurate than public MT options.

While many organizations wrestle with this dilemma, the challenge is especially felt in healthcare. The industry has intense volumes and varieties of data coming from multiple sources, including electronic health records, digital scans, genomic data, wearables and smartphone apps. The goal is to find a way to consistently produce data-driven insights at enterprise scale. This can be done with industrial machine learning (IML), which provides a scalable solution for ingesting data, building algorithms, deploying them into production, and generating continuous insights to ongoing business problems. In healthcare, IML makes possible the kind of personalized care that organizations are hoping to achieve, one that goes beyond predictive analytics to add context to large varieties of data and distill them into something actionable.