AI is transforming the travel commerce sector - IBM and Travelport partner on new platform
Delivered via the IBM Cloud...
IBM insists that a new artificial intelligence-based platform, developed with Travelport, will disrupt the travel industry.
Delivered via the IBM Cloud, IBM Travel Manager can track, manage, predict and analyse travel costs in one place using advanced AI, fundamentally changing how companies manage and optimise travel programmes.
It also provides cognitive computing, predictive data analytics and integrated travel and expense data to help business units control spend and enhance the end-traveler experience.
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Travelport, which had net revenues of $2.4bn in 2017, has a leadership position in airline merchandising, hotel content and distribution, car rental, mobile commerce and B2B payment solutions.
"IBM and Travelport are using the power of AI to unlock previously unavailable insights from multiple internal and external data sources," said Elizabeth Pollock, IBM Industry Client Leader for Travel & Transportation. "Travel managers can use this information to proactively drive improved supplier negotiations via real time and holistic data, enable budget holders to understand and change spending patterns, and improve travel policy compliance monitoring."
Fiona Shanley, Travelport’s Chief Customer and Marketing Officer, added: "While other solutions only provide a fragmented historical picture, IBM Travel Manager combines and normalises data from all key sources, allowing for more robust insights and benchmarking than other reporting solutions."
Google AI Designs Next-Gen Chips In Under 6 Hours
In a Google-Nature paper published on Wednesday, the company announced that AI will be able to design chips in less than six hours. Humans currently take months to design and layout the intricate chip wiring. Although the tech giant has been working in silence on the technology for years, this is the first time that AI-optimised chips have hit the mainstream—and that the company will sell the result as a commercial product.
“Our method has been used in production to design the next generation of Google TPU (tensor processing unit chips)”, the paper’s authors, Azalea Mirhoseini and Anna Goldie wrote. The TPU v4 chips are the fastest Google system ever launched. “If you’re trying to train a large AI/ML system, and you’re using Google’s TensorFlow, this will be a big deal”, said Jack Gold, President and Principal Analyst at J.Gold Associates.
Training the Algorithm
In a process called reinforcement learning, Google engineers used a set of 10,000 chip floor plans to train the AI. Each example chip was assigned a score of sorts based on its efficiency and power usage, which the algorithm then used to distinguish between “good” and “bad” layouts. The more layouts it examines, the better it can generate versions of its own.
Designing floor plans, or the optimal layouts for a chip’s sub-systems, takes intense human effort. Yet floorplanning is similar to an elaborate game. It has rules, patterns, and logic. In fact, just like chess or Go, it’s the ideal task for machine learning. Machines, after all, don’t follow the same constraints or in-built conditions that humans do; they follow logic, not preconception of what a chip should look like. And this has allowed AI to optimise the latest chips in a way we never could.
As a result, AI-generated layouts look quite different to what a human would design. Instead of being neat and ordered, they look slightly more haphazard. Blurred photos of the carefully guarded chip designs show a slightly more chaotic wiring layout—but no one is questioning its efficiency. In fact, Google is starting to evaluate how it could use AI in architecture exploration and other cognitively intense tasks.
Major Implications for the Semiconductor Sector
Part of what’s impressive about Google’s breakthrough is that it could throw Moore’s Law, the axion that the number of transistors on a chip doubles every five years, out the window. The physical difficulty of squeezing more CPUs, GPUs, and memory on tiny silicon die will still exist, but AI optimisation may help speed up chip performance.
Any chance that AI can help speed up current chip production is welcome news. Though the U.S. Senate recently passed a US$52bn bill to supercharge domestic semiconductor supply chains, its largest tech firms remain far behind. According to Holger Mueller, principal analyst at Constellation Research, “the faster and cheaper AI will win in business and government, including with the military”.
All in all, AI chip optimisation could allow Google to pull ahead of its competitors such as AWS and Microsoft. And if we can speed up workflows, design better chips, and use humans to solve more complex, fluid, wicked problems, that’s a win—for the tech world and for society.