In two previous posts, I covered the emerging industry of cloud-based machine learning solutions. First, I covered Microsoft's Azure Machine Learning and IBM's Watson Analytics. Microsoft's Azure ML provides a graphical drag-and-drop interface for connecting preprogrammed components of a data science pipeline together. The service is similar to KNIME and seemed targeted for users who knew just enough to know what to do, but not so much that they would want to code up fresh algorithms. One value added for Microsoft's product is a smooth integration for companies which already have their data stored in Microsoft's Azure compute cloud.

The adoption and application of Artificial Intelligence (AI) continues to accelerate at an exponential rate in modern businesses. As referenced in the 2017 Tech Trend Report, AI is nearing completion of the next layer in technological advancement, integrated into everything individuals and organizations do. This trajectory is predicted to drive cumulative worldwide spending of $40.6 billion on AI projects by 2024 – according to Raconteur. This is expected to create mass opportunity for the pioneering businesses currently investing in AI development. Moving beyond the hype in existing media coverage, this post will uncover the reality behind what AI means for businesses today, in the near future, and beyond 2017.

IBM Watson had been fully programmed to decode fashion images and the key aspects associated with them. It was intelligent enough to detect the face in the image, the pose of the model as well as body, colour, cut and silhouette of an outfit. It could also tell if two outfits were similar and also determine the dominant colours in an image. It then processed this unimaginably huge amount of raw data to come up with useful insights, which were then shared with the designer duo. Unbelievable as it may seem, Watson had managed to condense years and years of Bollywood fashion into carefully analysed and accurate information. From popular colours through Bollywood's timeline to prints and silhouettes that dominated a particular season, Falguni and Shane Peacock now had everything they needed to kickstart their ideation process.

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Sesame Workshop and IBM Watson today announced that they are creating a vocabulary app and the Sesame Workshop Intelligent Play and Learning Platform. The new platform will be used by Sesame Workshop and IBM to create a series of cognitive apps, games, and toys to help kids learn. This is the first public action announced by the partnership, which was formed more than a year ago. The platform will run on IBM Cloud and the partners invite the "ecosystem of software developers, researchers, educational toy companies, and educators to tap IBM Watson cognitive capabilities and Sesame Workshop's early childhood expertise to build engaging experiences to help advance children's education and learning," according to a statement from IBM Watson. "When we first started 45 years ago, [our show] was basically using the technology of the day -- that was TV -- to give all kids access to high-quality education," Sesame Workshop COO Steve Youngwood told VentureBeat in a phone interview.

Queensland-based Icon Group has announced plans to adopt IBM Watson for Oncology, an artificial intelligence (AI) and cognitive computing platform that will provide the organisation's oncologists with access to massive amounts of global research to help them better inform patient treatment plans. "Being in the cancer care sector, we've been watching with great interest the way that Watson for Oncology has been utilised in other markets," Cathie Reid, co-founder of Icon Group, told ZDNet. "There's an incredible amount of medical literature already available and that's growing at an exponential rate and being able to have that information available in a curated form at the touch of your fingertips is something that our physicians are very excited about.

We're starting to get a better picture of how artificial intelligence could help doctors better treat cancer. And in data presented at the American Society of Clinical Oncology meeting, IBM Watson Health gave a snapshot of how it's playing out so far. The studies looked at concordance rates, or how often Watson for Oncology reached the same course of treatment as the cancer doctors at different cancer centers around the world. At Manipal Comprehensive Cancer Center in India, for 112 cases of lung cancer, there was 96.4% concordance between Watson and the doctors. For 126 cases of colon cancer it was 81% of the time, and for 124 cases of rectal cancer cases were 92.7%.

We're starting to get a better picture of how artificial intelligence could help doctors better treat cancer. And in data presented at the American Society of Clinical Oncology meeting, IBM Watson Health gave a snapshot of how it's playing out so far. The studies looked at concordance rates, or how often Watson for Oncology reached the same course of treatment as the cancer doctors at different cancer centers around the world. At Manipal Comprehensive Cancer Center in India, for 112 cases of lung cancer, there was 96.4% concordance between Watson and the doctors. For 126 cases of colon cancer it was 81% of the time, and for 124 cases of rectal cancer cases were 92.7%.

Background: IBM Watson for Oncology is an artificial intelligence cognitive computing system that provides confidence-ranked, evidence-based treatment recommendations for cancer. In the present study, we examine the level of agreement for lung and colorectal cancer therapy between the multidisciplinary tumour board from Manipal Comprehensive Cancer Centre in Bangalore, India, and Watson for Oncology. Methods: Watson for Oncology is a Memorial Sloan Kettering Cancer Center (New York, USA) trained cognitive computing system that uses natural language processing and machine learning to provide treatment recommendations. It processes structured and unstructured data from medical literature, treatment guidelines, medical records, imaging, lab and pathology reports, and the expertise of Memorial Sloan Kettering experts to formulate therapeutic recommendations. Treatment recommendations are provided in three categories: recommended, for consideration and not recommended.

Artificial intelligence (AI) is rapidly moving from "nice to have" into "must have" territory for organizations. In fact, nearly six in ten AI early adopters think it will be a necessary element to remain competitive within the next few years. Two-thirds say AI is very important to their organization's strategy and success. AI can be a powerful tool for getting people the right information at the right time for decision making. By augmenting human intelligence and improving planning, AI has the potential to transform workplace productivity.