Agriculture companies are always striving to produce better tasting, longer lasting fruits and vegetables. Whether it's corn or berries, produce diminishes in value the minute it goes from the stalk or vine to the market. Driscoll's, a $3.5 billion provider of berry plants, is turning to emerging technologies, such as artificial intelligence (AI), machine learning (ML), the internet of things (IoT) and blockchain, to produce hardier plants and fortify its supply chain. "We're just scratching the surface on building an integrated data platform strategy that will take advantage of artificial intelligence and machine learning, both for R&D genetics and on the value chain of fruits as well as business operations," Driscoll's CIO Tom Cullen tells CIO.com. Driscoll's develops and leases strains of berry nursery plants -- strawberries, blueberries, blackberries and raspberries -- to growers around the world, from the Americas to New Zealand, China and Australia.

Hazel Savage is the CEO and Co-Founder of Musiio, an Artificial Intelligence company for A&R. Originally from the UK and having spent 5 years working in Australia she now resides in Singapore. With 12 years experience in the music industry, predominantly in tech companies, she boasts, Shazam, Universal, Pandora and BandLab as previous experience.

The tabu and restart are two fundamental strategies for local search. In this paper, we improve the local search algorithms for solving the Maximum Weight Clique (MWC) problem by introducing new tabu and restart strategies. Both the tabu and restart strategies proposed are based on the notion of a local search scenario, which involves not only a candidate solution but also the tabu status and unlocking relationship. Compared to the strategy of configuration checking, our tabu mechanism discourages forming a cycle of unlocking operations. Our new restart strategy is based on the re-occurrence of a local search scenario instead of that of a candidate solution. Experimental results show that the resulting MWC solver outperforms several state-of-the-art solvers on the DIMACS, BHOSLIB, and two benchmarks from practical applications.

The course combines elements of teaching, coaching and community. For this reason, the batch sizes are small and selective. I will be working with a small/selective group of people to actively transfer their career to AI through education and my network towards specific outcomes/goals. "Great course with many interactions, either group or one to one that helps in the learning. In addition, tailored curriculum to the need of each student and interaction with companies involved in this field makes it even more impactful. As for myself, it allowed me to go into topics of interests that help me in reshaping my career."

It's been a strong fortnight for machine intelligence fans, starting with Arm's Robert Elliot and Mark O'Conner publishing a white paper on the company's Arm NN machine learning platform and its optimisations for use on low-power embedded devices. "We expect machine learning to become a natural part of programming environments, with tiny embedded neural networks being part of program execution," the pair explain of the inspiration behind Arm NN. "To prepare for this, we've developed a low-overhead inference engine with the ability to import a file produced by a handful of machine learning frameworks. This supports a'write once, deploy many' approach to development, with the same framework able to target the Cortex-A class cores used in high-end mobile as well as the Cortex-M class cores used in processing environments with very small memories. We've spent significant effort to make sure that good performance is achieved on all of these processors It enables efficient translation of existing neural network frameworks, including TensorFlow and Caffe, allowing them to run efficiently, without modification, across Arm processing platforms. The inference engine can be distributed to different devices while taking advantage of the key optimizations of each."

Image provided by Nearmap Australia. The dust is finally settling after another stellar Locate conference, and four phenomena revealed themselves as ubiquitous, rapidly developing and happening right now. Locate co-located with Geosmart Asia for 2018 to provide a bustling, inspiring three days of presentations, networking, and socialising for delegates at the Adelaide Convention Centre. Whilst being physically impossible to attend all the promising sessions across the seven concurrent tracks presented in the event's main program, certain developments, trends and technologies revealed themselves as ubiquitous over the course of the conference. Below we summarise some of the key take-aways and trends of this year's Locate -- ideas and developments that wove themselves through a myriad of applications, technologies and disciplines, and will continue to make their presence felt in the years to come.

In this work, we focus on the task of generating natural language descriptions from a structured table of facts containing fields (such as nationality, occupation, etc) and values (such as Indian, actor, director, etc). One simple choice is to treat the table as a sequence of fields and values and then use a standard seq2seq model for this task. However, such a model is too generic and does not exploit task-specific characteristics. For example, while generating descriptions from a table, a human would attend to information at two levels: (i) the fields (macro level) and (ii) the values within the field (micro level). Further, a human would continue attending to a field for a few timesteps till all the information from that field has been rendered and then never return back to this field (because there is nothing left to say about it). To capture this behavior we use (i) a fused bifocal attention mechanism which exploits and combines this micro and macro level information and (ii) a gated orthogonalization mechanism which tries to ensure that a field is remembered for a few time steps and then forgotten. We experiment with a recently released dataset which contains fact tables about people and their corresponding one line biographical descriptions in English. In addition, we also introduce two similar datasets for French and German. Our experiments show that the proposed model gives 21% relative improvement over a recently proposed state of the art method and 10% relative improvement over basic seq2seq models. The code and the datasets developed as a part of this work are publicly available.

Blend two or three metals together and you get an alloy that usually looks and acts like a metal, with its atoms arranged in rigid geometric patterns. But once in a while, under just the right conditions, you get something entirely new: a futuristic alloy called metallic glass that's amorphous, with its atoms arranged every which way, much like the atoms of the glass in a window. Its glassy nature makes it stronger and lighter than today's best steel, plus it stands up better to corrosion and wear. Even though metallic glass shows a lot of promise as a protective coating and alternative to steel, only a few thousand of the millions of possible combinations of ingredients have been evaluated over the past 50 years, and only a handful developed to the point that they may become useful. Now a group led by scientists at the Department of Energy's SLAC National Accelerator Laboratory, the National Institute of Standards and Technology (NIST) and Northwestern University has reported a shortcut for discovering and improving metallic glass – and, by extension, other elusive materials - at a fraction of the time and cost.

Donald Trump has hammered Amazon.com Inc.'s share price in recent weeks by trying to pick fights online with Chief Executive Officer Jeff Bezos. But the actual object of his Twitter onslaught is the domain of Jeff Wilke, Bezos' right-hand man for most of the past 18 years. Wilke runs Amazon's worldwide consumer division, meaning he's in charge of both selling people stuff and figuring out how to deliver the items as efficiently as possible. Often, the company uses the U.S. Postal Service.

When it comes to robots stealing our jobs, there's one task that plenty of people would be happy to relinquish: assembling flat-pack furniture. The day we can handball that job off is a step closer. In Science Robotics today, a trio of roboticists from Nanyang Technological University in Singapore unveiled a semi-autonomous pair of robotic arms that can put together the frame of an IKEA chair. Made from off-the-shelf gear, their invention can look at chair parts scattered around, grasp and lift the right bit, carefully insert wooden pins in pre-drilled holes and slot the pieces together. And while the robot's repertoire of furniture-building skills is limited -- it can't yet screw in metal screws, for instance -- the technology could soon be ready for jobs that require human-like dexterity, such as electronics and aircraft manufacturing.