Machine learning and artificial intelligence pose the ability for global health practitioners to glean new insights from data they are already collecting as part of implementing their programs. However, little practice-based research has been documented on how to incorporate machine learning into international development programs. Current systems mirror in form and format the use of manually completed paper records to create periodic reports for leadership. This has vexed health officials with a proliferation of systems leaving some "data rich, but information poor". Yet the growth of available analytical systems and exponential growth of data require the global digital health community to become conversant in this technology to continue to make contributions to help fulfill our missions.

When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage. Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque. In this article, I cover six ways that machine learning threatens social justice and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism.When you harness the power and potential of machine learning, there are also some drastic downsides that you've got to manage. Deploying machine learning, you face the risk that it be discriminatory, biased, inequitable, exploitative, or opaque. In this article, I cover six ways that machine learning threatens social justice and reach an incisive conclusion: The remedy is to take on machine learning standardization as a form of social activism.When you use machine learning, you aren't just optimizing models and streamlining business.

According to PwC, AI's potential global economic impact will reach USD 15.7 trillion by 2030. However, the enterprises who look to deploy AI are often hampered by the lack of time, trust and talent. Especially, with the highly regulated sectors such as healthcare and finance, convincing the customers to imbibe AI methodologies is an uphill task. Of late, the AI community has seen a sporadic shift in AI adoption with the advent of AutoML tools and introduction of customised hardware to cater to the needs of the algorithms. One of the most widely used AutoML tools in the industry is H2O Driverless AI.

The Internet of Things is gradually penetrating every aspect of our lives. With the growth in numbers of internet-connected sensors built into cars, planes, trains, and buildings, we can say it is everywhere. Be it smart thermostats or smart coffee makers, IoT devices are marching ahead into mainstream adoption. But, these devices are far from perfect. Currently, there is a lot of manual input required to achieve optimal functionality -- there is not a lot of intelligence built-in.

In this video from Intel AI DevCon 2018, Andrew Ng from Deeplearning.ai and Landing.ai When you ask Siri for directions, peruse Netflix's recommendations or get a fraud alert from your bank, these interactions are led by computer systems using large amounts of data to predict your needs. The market is only going to grow. By 2020, the research firm IDC predicts that AI will help drive worldwide revenues to over $47 billion, up from $8 billion in 2016. Still, Andrew NG says fears that AI will replace humans are misplaced: "Despite all the hype and excitement about AI, it's still extremely limited today relative to what human intelligence is."

Chances are that you cannot scroll through your inbox each morning without running across at least one email trumpeting the impending arrival of artificial intelligence, blockchain and machine learning. They are just two of today's hot topics in a long list of new technologies that promise to radically transform the way business is done -- much as electricity, production lines, computers, and the internet transformed business and society in days past. As a long-time innovator for businesses, SAP is keenly focused on utilizing breakthrough technologies to help companies of all stripes deliver on their digital transformation goals. SAP has been researching the topic extensively, both from a pure technology standpoint and from the perspective of business value. Results from SAP's latest global survey of business leaders show two big hurdles to adoption.