Embedded from A/B testing software
Venture capital investors have lately taken a keen interest in the logistics, supply chain management and shipping market, which measures in the trillions of dollars globally and in the hundreds of billions of dollars in the US alone.
Based on data from 502 deals struck with US-based companies in the supply chain management, shipping and logistics industries, it’s easy to see that investor interest has been piqued by this supposedly “boring” space. Over the past several years, there’s been a significant upswing in the amount of capital deployed into upstart logistics, shipping and supply chain management companies… Between the start of 2013 and the end of 2016, the amount of venture capital money invested into these industries essentially tripled, a positive change of 297% in the space of four years.
The scale, scope and depth of data supply chains are generating today is accelerating, providing ample data sets to drive contextual intelligence. The following graphic provides an overview of 52 different sources of big data that are generated in supply chains Plotting the data sources by variety, volume and velocity by the relative level of structured/unstructured data, it’s clear that the majority of supply chain data is generated outside an enterprise. Forward-thinking manufacturers are looking at big data as a catalyst for greater collaboration. Source: Big Data Analytics in Supply Chain Management: Trends and Related Research. Presented at 6th International Conference on Operations and Supply Chain Management, Bali, 2014
At the 2017 MIT Tech Conference, Roboticist Helen Greiner, CTO of CyPhy Works, talked about her vision of an end-to-end automation of the supply chain, making it much more efficient than it is today. Greiner: “Up above the treetops is a highway waiting to be populated.”
CyPhy Works and Pilot Thomas Logistics are working together to bring innovative technology to the oil and gas industry. PTL is adding our persistent aerial solutions to their portfolio of services. We flew above PTL’s Mobile, Alabama facility to show their team what is possible with PARC.
This highlight reel includes targeted zoom to a bridge 3 miles away, standard and IR overviews of a tank farm, and vehicle tracking on both land and water. All this during roughly 25 mph winds; conditions that could down other drones.
PARC’s live feed was seen simultaneously at each company’s headquarters (in Massachusetts and Fort Worth, Texas) through CyPhy’s data platform. The next day a live feed was also streamed to IHS Markit’s CERAWeek – the energy industry’s premier annual conference – as part of CyPhy Work’s Energy Innovation Pioneers presentations.
Source: CB Insights
Venture Radar: Chatbots are programs that mimic conversation with people using artificial intelligence.
CB Insights: Advances in artificial intelligence algorithms have put chatbots and voice assistants in the spotlight, with investor interest in the space increasing in recent months.
At Inbound 2016, HubSpot’s co-founders Brian Halligan and Dharmesh Shah entertained 19,000 attendees with their take on the past and future of marketing. Here’s what I learned from their keynote presentation and a brief interview.
2017 will be the year of the bot. So predicts Halligan, adding “in five years, you will do a lot less navigating through apps and more just asking questions and chatting back and forth with bots… the next thing you know, we like it and it’s easier and more efficient than waiting for the sales rep to call you back.” Shah notes that businesses started building websites in the 1990s so they can answer customer questions 24/7. “Soon,” he says, “they will start building bots. They won’t replace the websites, but they will power them. The shortest time between a customer question and the answer will be a bot. It’s not human vs. bot, it’s human to the bot powered.” (HubSpot’s recent contribution to the bot power movement: Growthbot).
The “marketing conversation” will become a human-machine conversation. That the essence of marketing is a “conversation” between a business (or any “brand”) and its customers and potential customers has been a marketing tenet (and cliché) for a long time. While that conversation has been conducted over the last twenty years increasingly through a computer screen with the help of a keyboard, it is now transforming into human-machine conversation. “The conversational UI,” says Shah, “is going to be an even bigger leap in software than we had with the shift to Web-based software. We are all re-thinking now how to build products.” It’s the most natural way to engage, interact, market and sell: “We will have voice input because it’s much more efficient [than typing] and visual output because it’s more efficient than listening—we can see and read and scan much faster that we can listen. I don’t think screens are going away but the keyboard is likely going to be less and less prevalent.”
Venture funding for robotics has exploded by more than 10x over the last six years and shows no signs of stopping. Most of this investment has been focused on the usual suspects: logistics, warehouse automation, robot arms for manufacturing, healthcare and surgical robots, drones, agriculture, and autonomous cars…
There’s a massive, untapped market… Commercial spaces such as hotels, hospitals, offices, retail stores, banks, schools, nursing homes, schools, malls, and museums.
Intel’s CEO Brian Krzanich explained why the company was so keen to move into the world of self-driving cars.
“Many of you have asked why we think autonomous cars and vehicles are so important to Intel’s future,” Krzanich said in an email message to Intel employees on Monday.
“The answer is DATA. Our strategy is to make Intel the driving force of the data revolution across every technology and every industry. We are a DATA company. The businesses we focus on, and deliver solutions to, create, use and analyze massive amounts of data,” he wrote.
Source: Times of Israel
TensorFlow has become the most popular AI programming project on software code sharing service GitHub, leapfrogging well-regarded systems created by universities and corporate rivals, according to data gathered by Bloomberg.
Deep Learning has had a huge impact on computer science, making it possible to explore new frontiers of research and to develop amazingly useful products that millions of people use every day. Our internal deep learning infrastructure DistBelief, developed in 2011, has allowed Googlers to build ever larger neural networks and scale training to thousands of cores in our datacenters. We’ve used it to demonstrate that concepts like “cat” can be learned from unlabeled YouTube images, to improve speech recognition in the Google app by 25%, and to build image search in Google Photos. DistBelief also trained the Inception model that won Imagenet’s Large Scale Visual Recognition Challenge in 2014, and drove our experiments in automated image captioning as well as DeepDream.
While DistBelief was very successful, it had some limitations. It was narrowly targeted to neural networks, it was difficult to configure, and it was tightly coupled to Google’s internal infrastructure – making it nearly impossible to share research code externally.
Today we’re proud to announce the open source release of TensorFlow — our second-generation machine learning system, specifically designed to correct these shortcomings. TensorFlow is general, flexible, portable, easy-to-use, and completely open source. We added all this while improving upon DistBelief’s speed, scalability, and production readiness — in fact, on some benchmarks, TensorFlow is twice as fast as DistBelief…
TensorFlow is great for research, but it’s ready for use in real products too. TensorFlow was built from the ground up to be fast, portable, and ready for production service. You can move your idea seamlessly from training on your desktop GPU to running on your mobile phone. And you can get started quickly with powerful machine learning tech by using our state-of-the-art example model architectures. For example, we plan to release our complete, top shelf ImageNet computer vision model on TensorFlow soon.
But the most important thing about TensorFlow is that it’s yours. We’ve open-sourced TensorFlow as a standalone library and associated tools, tutorials, and examples with the Apache 2.0 license so you’re free to use TensorFlow at your institution (no matter where you work).