It is efficient for organizations to connect business knowledge with data-guided solutions. Data science enables businesses to derive data-driven decisions. Data, and more importantly analytics, are changing the way we see our machines, our processes and our operations. The fusion of data science with other technologies would enhance the decision-making approach, making it more eloquent and accurate. Let's study in brief, how this fusion helps different industry verticals.

At Reltio we've been articulating a vision, which includes a pragmatic perspective of machine learning (ML) for over 3 years now. Realizing that not only does ML not offer a silver bullet, but there is still much to learn (pun intended) as to how such technologies can ultimately benefit both IT and business. Noted Big Data expert Bernard Marr provides a nice list of use cases that might be applied to your specific business and industry. The key is that a focused set of benefits for each users' role, must be defined in order for it to be accurately measured so it doesn't get labelled yet another (data) science project with limited value. Most companies are NOT ready for any form of AI, ML, or Cognitive Computing to help their business user, because their data is such poor shape to even attempt such an endeavor.

I like to keep up with how cognitive computing is doing in the marketplace and came across a TedMedLive Talk by Basit Chaudhry, an MD specializing in the design of clinical service delivery systems for chronic disease care. His talk is a little dated, 2013, but what he had to say then applies very much to the state of today's healthcare industry. "It's not possible [anymore] to try to keep up with everything that's going on, ... for one person to fit everything known in medicine inside of their head, regardless of how talented they are," Chaudhry said. The "breathtaking growth of medical knowledge," he said, has forced clinicians to specialize so they can cope with their own cognitive limits and focus on a subset of all things medical. As a result, the quality of clinical care has suffered, according to Chaudhry, who is the founder of Tuple Health and a former IBM medical scientist.

Are computers going to take over the world? Not anytime soon if businesses commit to infusing cognitive computing into the DNA of each IT practitioner and changing the culture of IT organizations and the business they serve. The highest levels of analysis and computer learning need a corpus curated by subject matter experts in each of the traditional towers, such as the server, storage and network. While the central IT organization may create a common set of cognitive tooling, each tower should be responsible for populating its area's content. Population may fall to the senior tower practitioners.

Firstly, artificial intelligence does not work at mimicking human thought processes. The concept behind AI is to not mimic human thought and processes, but to solve a problem through the use of the best possible algorithm. This can be illustrated through an example of a car, which stays on course and avoids a collision. The processes in AI are not looking to process data in the same way as it would be processed by humans, but they're looking to process it through the best known algorithm present. Processing data the way humans do it is a far more fault-prone and complex algorithm.

Today, AI can be consumed in two forms – 1) cognitive APIs, and 2) custom ML models. IBM Watson, Google Cloud ML APIs, Microsoft Cognitive Services and Amazon AI APIs are examples of such hosted APIs. Any developer with the basic understanding of consuming REST API can invoke these services to add intelligence to her application. But these services come with a limitation – they are trained on generic datasets. That means while computer vision API can identify a car as an object, it cannot detect its make and model.

There is growing need for robots that can interact with people in everyday situations. For service robots, it is not reasonable to assume that one can pre-program all object categories. Instead, apart from learning from a batch of labelled training data, robots should continuously update and learn new object categories while working in the environment. This paper proposes a cognitive architecture designed to create a concurrent 3D object category learning and recognition in an interactive and open-ended manner. In particular, this cognitive architecture provides automatic perception capabilities that will allow robots to detect objects in highly crowded scenes and learn new object categories from the set of accumulated experiences in an incremental and open-ended way. Moreover, it supports constructing the full model of an unknown object in an on-line manner and predicting next best view for improving object detection and manipulation performance. We provide extensive experimental results demonstrating system performance in terms of recognition, scalability, next-best-view prediction and real-world robotic applications.

Healthcare is in digital transformation, both on the healthcare provider and payer side. Research indicates that healthcare delivery now is ready to be disrupted by artificial intelligence (AI) and AI start-ups between 2017 and 2022. Annual spending on computer aided diagnosis (CAD) systems and evolutions in this field will be impacted as a result. Computer aided diagnosis systems are expected to enhance speed and accuracy of patient diagnoses and reduce pressure on doctors, among others by better accuracy of the CAD systems. Artificial intelligence increasingly won't just be significant in CAD systems, it is becoming important across the several other technologies driving the digital healthcare market.

Over the years, the advances in computing and computing systems have delivered tremendous progress, development and benefits to individuals and entities across nations: its government, industries, organizations and academia (NGIOA). As they continue to do so; the emerging computing systems, cognitive systems, are expected to forever change the way we the humans will interact with computers and computing systems in all formats. The rise of "cognitive computing" brings us a new beginning, a new age, where computers with human like intelligence and cognitive abilities, works hand in hand with humans and human intelligence in solving complex problems facing humanity in cyberspace, geospace and space (CGS). As computers begin to understand humans and think like human beings, they will undoubtedly increase human intelligence, human capabilities, reach and knowledge that would allow them to explore more, understand more, make accurate predictions and draw intelligent conclusions. So, does this mean that we are moving into an era where computers can augment human knowledge, ingenuity, cooperation and collaboration in entirely new ways?

Cognitive computing and machine learning are going to transform knowledge management. Chatbots, cognitive search, natural language processing (NLP), and semantic technologies accelerate the ability of humans to find what they need to do their jobs. But to foster an intelligence-driven organization that can handle a broad range of topics, the underlying search technology must be extremely robust. KMWorld recently held a webinar featuring Paul Nelson, innovation lead, Accenture Analytics; and Scott Parker, senior product marketing manager, Sinequa, who discussed how cognitive computing is changing knowledge management and what to do about it. Natural language processing is everywhere, Nelson explained, and the five main technologies utilizing this are chatbots, question answer, semantic search, fact extraction, and classification.