As you may read from our first article farming robots are shaping agriculture and will feed humans of the future. Economics will demand a leap from theoretical concept of artificial intelligence to its practical application in agriculture, many experts suggest. But this process has already begun and is irreversible. Automated irrigation systems, crop health monitoring, face recognition systems for domestic cattle, CBR systems for fishing industry and many others are clear examples of how AI can be the Holy Grail for the farming industry. Irrigation systems are as old as man itself since agriculture is the foremost occupation of civilized humanity.

IBM will launch a Korean version of its AI platform Watson next year in cooperation with local IT service vendor SK C&C, the companies have announced. SK announced Monday that it signed a cooperation agreement with Big Blue on May 4 and will together build an integrated system to market Watson in South Korea. They will develop Korean data analysis solutions based on machine learning and natural language semantic analysis technology for Watson within this year, and will commercialise it sometime in the first half of 2017, SK said. IBM and SK will also build a "Watson Cloud Platform" at the Korean company's datacentre in Pangyo -- the local version of Silicon Valley -- that IT developers and managers can access to make their own applications. For example, an open market business can apply the Watson solution to its product search features to make a personalized contents recommendation solution.

The first Monday in May brings one of the marquee fashion events of the year -- the Met Gala. Held at the Metropolitan Museum of Art in New York City as a benefit for the museum's Costume Institute, this year's gala comes with an unexpected high-tech twist. The theme of the evening, and the accompanying museum exhibition, is "Manus x Machina: Fashion in an Age of Technology." In keeping with the theme is a rather unlikely collaboration -- IBM is joining forces with the fashion house Marchesa, known for its whimsical, romantic designs. For Monday's event, Marchesa designers and co-founders Georgina Chapman and Keren Craig teamed up with IBM's cognitive computing system Watson to design a "cognitive dress" that will be worn by a yet-to-be-named model.

Reinforcement learning (RL) is a popular choice for solving robotic control problems. However, applying RL techniques to controlling humanoid robots with high degrees of freedom remains problematic due to the difficulty of acquiring sufficient training data. The problem is compounded by the fact that most real-world problems involve continuous states and actions. In order for RL to be scalable to these situations it is crucial that the algorithm be sample efficient. Model-based methods tend to be more data efficient than model-free approaches and have the added advantage that a single model can generalize to multiple control problems. This paper proposes a model approximation algorithm for continuous states and actions that integrates case-based reasoning (CBR) and Hidden Markov Models (HMM) to generalize from a small set of state instances. The paper demonstrates that the performance of the learned model is close to that of the system dynamics it approximates, where performance is measured in terms of sampling error.

This paper presents a novel approach to retrieval in process-oriented case-based reasoning (POCBR) which considers the adaptability of workflows cases during the retrieval phase. A novel concept of adaptability in POCBR is proposed, which assesses the potential similarity increase of a case which can be gained by adaptation. The adaptability of a case is learned from the case base in an off-line pre-processing phase prior to the retrieval. The proposed approach is generic as it can be used in combination with different adaptation methods. An empirical evaluation in the domain of cooking workflows demonstrates the benefit of the approach.

Explanations play an important role in AI systems in general and case-based reasoning (CBR) in particular. They can be used for reasoning by the system itself or presented to the user to explain solutions proposed by the system. In our work we investigate the approach where causal explanations are automatically extracted from textual incident reports and reused in a CBR system for incident analysis. The focus of this paper is evaluation of such explanations. We propose an automatic evaluation measure based on the ability of explanations to provide an explicit connection between the problem description and the solution parts of a case.

IBM Japan has teamed up with Subaru to investigate how its Watson Supercomputer could help improve the automaker's EyeSight driver assist technology. As well as developing a data analytics system, the two companies are keen to integrate cloud and artificial intelligence technologies, which bodes well for the ongoing development of autonomous, networked cars. The benefits of networked autonomous vehicles were recently demonstrated by the European Truck Platooning Challenge, where teams of autonomous trucks made their way from their respective factories to Rotterdam. As well as demonstrating the fact autonomous vehicles can effectively make long trips without causing the end of the world (shocking, we know), the trucks were able to maintain a gap of just 15 m (49 ft) and react to sudden braking manoeuvres in just 0.1 seconds thanks to a WiFi connection keeping them all linked. Daimler has also invested in Car-to-X technology, which features in its latest E-Class.

You may remember how, back in 2011, IBM's supercomputer Watson competed against the world's best "Jeopardy" champions and won. Fast-forward a few years and cognitive computing and machine learning have become the latest tech buzzwords that promise to revolutionize industries such as healthcare by providing real-time, actionable insights and much more. Entire markets, including banking and finance, law, and auditing and accounting, to name a few, are also facing disruption as well as opportunities with ongoing advancements in cognitive technology. The power of IBM Watson is finally being realized now that it can understand, reason and learn from the wealth of big data that most businesses are struggling to make sense of. Early childhood education appears to be next on the agenda.

Officially, fashion's biggest party is a fund-raising event for the Metropolitan Museum of Art's fashion department, but ever since Anna Wintour, Vogue's high-powered editor, took over as chairwoman in 1999, it has turned into the industry's equivalent of the Oscar's red carpet, bringing together an invite-only list of dressed-up celebrities and bigwigs. This year, as everyone was asked "Who are you wearing?" one attendee--model Karolina Kurkova--got to say IBM Watson, in collaboration with high-fashion label Marchesa. Covered in fabric flowers embedded with LEDs, the "cognitive dress" was light, elegant, and romantic, as is Marchesa's signature. It continually changed color with the help of Watson's powerful analytical technology, tying it perfectly into the theme of this year's fashion exhibit at the Met, called "Manus x Machina: Fashion in an Age of Technology." But while the dress was beautiful, the lights were sometimes overbearing.

At last night's Met Gala, the lavish annual fashion event hosted by Vogue, model Karolina Kurkova wore a dress that was half man-, half machine-made. The "Cognitive Dress"--perhaps one of the least fashion-forward names found on the red carpet--is the product of a partnership between British design studio Marchesa and Watson, IBM's friendly cognitive computer. The gown, a white tulle design embroidered with 150 LED-connected flowers, is an interesting glimpse of how humans and machines can work together to create something that otherwise wouldn't be possible. To design the dress, Marchesa's designers first chose five sentiments they wanted the dress to express: joy, patience, excitement, encouragement, and curiosity. Then they fed two datasets into IBM's Cognitive Color Tool, a program that uses color psychology to match emotion to hues.