Can FinTech and AI solve the ESG data puzzle?

Here's something written by the AI at articoolo.com. Not bad, these AI's are best at summaries and press releases, but they keep getting better. We hear a great deal about Artificial Intelligence today, but several people truly realize what artificial intelligence actually is. Even more perplexing to beginners is that many people which are in the AI field argument its definition. Still compounding the problem is when commercial endeavors start touting their technology as unnaturally intelligent driven, when really they'ren't.

It's a competitive real-estate rental market out there, and Trulia wanted to capitalize on the interest with a new Facebook Messenger bot it launched earlier this month. The bot lets users search for rental properties and keep up to date on new properties when they become available. Trulia's bot came out of a quarterly hackathon project hosted by at real estate tech firm this past May, and the company learned a lot about bot-building. The experience showed that businesses should give bot-making a shot, even if they're not tech companies, said Yardley Ip, general manager for Trulia Rentals. "Given that the tools are so easy to use, and it's so lightweight to develop [a bot], I think businesses should try it," Ip said.

The hire marks the start of the Alphabet ( GOOG)-owned, Art Levinson-helmed biotech's drive to build out a computational biology and machine learning team. Calico will tap into that expertise--and that of the machine learning team it has tasked Koller with building--to advance its drive to understand aging and, in doing so, enable people to live longer, healthier lives. "Daphne and her team will work in close collaboration with the basic and translational scientists at Calico and partner with other machine learning experts, including the team at Google, to derive novel insights and effective interventions," Calico R&D President Hal Barron said. The plan now is to add more staff to work with Koller, including a machine learning engineer and someone capable of developing algorithms to analyze biological images and videos.

During the 20th century, advances in fertilizers, irrigation, and mechanized farming technology helped make it possible to feed a dramatically growing world population. Now, advocates say, the next big advance in agricultural technology may come from the digital world, as modern computer vision, precision sensors, and machine-learning technology help farmers use last century's advances more efficiently and precisely to grow healthier and tastier food. "We're at the cusp of this next wave of innovation in agriculture, which we call digital agriculture," says Mike Stern, the president of The Climate Corporation. "It has to do with, over the past five to seven years, the farm really digitizing, not unlike how our society has changed in terms of the tools and types of things we can do." The Climate Corp., which was purchased by agriculture giant Monsanto for roughly 1 billion in 2013, is one of several companies working to build a digital analytics hub for farmers, merging images from satellites, drones, and cameras, as well as readings for everything from soil thermometers to tractors' on-board computers.

In the words of Seth Godin, "Marketing is no longer about the stuff that you make, but about the stories you tell." Until now, marketers had two choices: they could either pick personalized, or scalable, but not both; they could opt for inefficient manual ways of personalized engagement, or choose static digital marketing that scaled but took a one-size-fits-all approach. Machine intelligence is changing that. AI-driven brand storytelling intelligently adapts to a consumer's unique preferences and behavior to deliver truly personalized stories at scale, by combining the human elements of storytelling with machine-powered personalization. Traditional human-driven storytelling follows a preset pattern.

The world of bots is exploding. Today's bots integrate within existing applications and replicate the support and intelligence of their human counterparts. They can simplify myriad functions, enabling consumers to gather information or fulfill requests on command. They empower us to make decisions more quickly by sharing information rapidly and clearly in a conversational format. With close to a billion people using Facebook messenger every month, many will soon use bots on the platform to find out when a restaurant opens, place an order for a T-shirt, or even pay their bills.

In movies artificial intelligence is usually depicted with sci-fi images of robots, cyborgs and other dehumanized creatures. For many, the word is misunderstood and creepy. The truth is AI is helping us live more self-actualized lives in more ways than we can imagine. The intersection of AI technology and human communication is a hotbed of activity as chatbots and virtual assistants become requirements for everyday tasks and productivity. The artificial intelligence that powers so many of the products we know and love can be divided into two categories: conversational and experiential. Customer service chatbots, Siri, airline phone menus and even Google are great examples of the types of conversational AI we encounter in day-to-day life.

This week saw the eruption of a vendor spat when NVIDIA, developer of GPUs widely used in the AI/machine learning market, alleged foul play against Intel in recent comparative benchmark results involving Intel's Xeon Phi processors. While NVIDIA makes some fair points about the methodology employed by its rival, Intel announced impressive news of its own on the AI/Xeon Phi front. First, let's look at NVIDIA's criticism of Intel that came in the form of a blog post by Ian Buck, vice president of accelerated computing, who declared that Intel compared Xeon Phi to out-of-date NVIDIA benchmarking data. "Intel used Caffe AlexNet (benchmarking) data that is 18 months old, comparing a system with four (NVIDIA) Maxwell GPUs to four Xeon Phi servers," Buck wrote. "With the more recent implementation of Caffe AlexNet, publicly available here, Intel would have discovered that the same system with four Maxwell GPUs delivers 30 percent faster training time than four Xeon Phi servers. In fact, a system with four Pascal-based NVIDIA TITAN X GPUs trains 90 percent faster and a single NVIDIA DGX-1 is over 5x faster than four Xeon Phi servers."