Amazon is using artificial intelligence (AI) to better understand search queries and why a person may be looking for something.
Understanding why a customer searches for a product is just as import as knowing what they searched for. Knowing the context can help a retailer make relevant recommendations for other products that not only compliment the item being searched for, but the activity or reason behind the search. Amazon is intent on cracking that piece of the puzzle, and is applying AI to the problem.
“In a paper accepted to the ACM SIGIR Conference on Human Information Interaction and Retrieval, my colleagues and I present a new neural-network-based system for predicting context of use from customer queries,” writes Adrian Boteanu. “From the query ‘adidas mens pants’, for instance, the system predicts the activity ‘running.’
“In tests, human reviewers agreed, on average, with 81% of the system’s predictions, indicating that the system was identifying patterns that could improve the quality of Amazon’s product discovery algorithms.”
As Amazon continues to improve its algorithms, shoppers should see increasingly relevant shopping recommendations and the research could open a whole new arena for personalized digital shopping assistants.
China may have finally turned a corner in its fight against the coronavirus, and it has robots to thank for helping it do so.
Throughout the coronavirus pandemic China has issued cutting edge technology in an effort to combat the virus. Early on Chinese authorities used AI-driven robots to scold people for being in public without wearing masks, while companies worked on using drones to deliver medicine and supplies to patients without endangering healthy people.
As China has continued to fight the pandemic, robots continue to play a significant role in the ongoing efforts to contain the spread of the virus. According to All About Circuits, in addition to the robots scolding people for not wearing masks, authorities are also relying on robots that scan crowds for raised temperatures, one of the earliest symptoms of infection. The robots “include high-resolution cameras and infrared thermometers that are capable of scanning the temperatures of up to 10 people at once who are in a radius of 5 meters. If one of these robots discovers somebody who is not wearing a mask or who has a high temperature, an alert is sent to the authorities.”
China’s success with robots is only possible due to a combination of 5G, AI, edge computing, cloud computing and IoT. The end result are robots capable of interacting with people in a way never before seen. As All About Circuits highlights, that interaction can be eerily lifelike:
“You there! The gentleman wearing a red coat holding an umbrella in your left hand—yes, you. You are not wearing a face mask, please put one on immediately. If you do not have one, come to the police car and we will provide you with one.”
With robots finally beginning to deliver on the promise proponents have long held out, it’s little wonder Microsoft, AT&T, Google Cloud and Verizon are all working hard to capitalize on the emerging technologies.
Would it surprise you to learn that by next year a quarter of the world’s population is expected to be a mobile gamer? Cell phones are everywhere, and even in developing nations smartphones are a lifeline to services that would otherwise be difficult to reach by traditional methods. It comes as no surprise, then, in a world where over 60% of the people in the world have a cell phone that many of them are using them for entertainment. After all, it’s your payment method, your camera, and more on top of being your primary means of communication to the outside world. Mobile gaming is on the rise, and the latest technology is making it better than ever.
Mobile gaming has been hugely popular even since the days of snake on your old flip phone. Once smartphones became more prevalent we started to see the rise of games like Candy Crush and Words with Friends. With 10 million downloads, Candy Crush Saga has garnered $71 million. Fortnite was released on iOS in March of 2018, and by December of that year it had been downloaded 83 million times and boasted a monthly rvenue of $64 million. In 2019 Clash of Clans was the top grossing game in the App Store, generating $1.54 million in sales every single day.
By next year, mobile gaming is expected to rake in $90 billion. Engagement with mobile gaming grows by 10% every single year. As people become accustomed to the new way of gaming, which requires downloads, accounts, online play, and in-app purchases, the revenue will roll in faster and in greater volume.
The pressure is on to get these apps to market as quickly as possible in order to get a piece of the revenue pie. Artificial intelligence is being applied to not only the development of games, but also to the testing and to the gameplay, as well.
AI can be used to fill in maps based on developer specifications. Once the gameplay is developed, AI can be used to test the gameplay in a fraction of the time it would otherwise take developers – from weeks to check one map down to less than an hour.
Self-learning AI in video games will also greatly enhance the gameplay, leading to a more challenging game for players and greater personalization. Artificial intelligence can learn from a player’s gameplay and responses to create more detail and harder challenges. It can help characters to learn and grow from the player and personalize gameplay to whatever players like or expect.
Artificial intelligence is driving innovation in mobile gaming while also powering the development of new games. AI can burn through 100 human lifetimes’ worth of experience in a single day, making this a valuable tool to augment human game developers.
AI game testing is one of the biggest time-savers in mobile gaming development right now, taking on the testing of speed and performance while looking for glitches and other issues in gameplay. It can help speed development time and get games to market faster. Learn more about the use of AI in mobile gaming below.
Individuals concerned about whether they have coronavirus will be able to get a virtual checkup just by asking Siri.
Apple has updated Siri to walk individuals through the U.S. Public Health Service questions to determine their risk and whether they need to take further action or wait it out.
“Hey Siri! Do I have coronavirus?” will prompt the digital assistant to ask the following:
Siri Coronavirus Response 1
Depending on the answer, Siri will respond with:
Siri Coronavirus Response 2
Again, depending on the answer, Siri may respond with:
Siri Coronavirus Response 3
This is just the latest example of how technology can be used to assist over-worked medical staff. Virtual assistants and artificial intelligence can act as a sort of early triage, helping individuals know when they should seek medical attention and, at the same time, save medical professionals from individuals who may be worried about nothing.
“There’s been a lot of advances in machine learning that take things that would have been literally impossible ten years ago and made those things much more possible today,” says Etsy CEO Josh Silverman. “With 62 million products for sale, picking for any given buyer the 20 or 30 that should be on page one of search results is a pretty interesting and pretty challenging task. The key is understanding what an item is with relatively little data and then being able to determine for each individual person how to personalize search results.”
Josh Silverman, CEO of Etsy, discusses how Etsy has increased growth by standing out in a world of sameness and by employing machine learning technology to personalize the Etsy experience for their customers. Silverman talks about his strategy for success in an interview with Fortune:
We Started Doing Much Fewer Things Much Better
Etsy has never been more relevant. In a world where so many of our products are being commoditized and we’re surrounded by a sea of sameness, Etsy stands for something really different. I think it’s really important that we stand out in the world and I’m proud of what the team has done to achieve that. The definition of success was really clear. I think from day one it’s about growing the size of the pie for everyone. The actual tactics that it was going to take to do that we’ve learned together as a team over time.
When I arrived, there were maybe eight or ten different metrics of success that we all held relatively equally. I said there’s one metric that matters much more than every other, which is what we call gross merchandise sales. In other words, the total sales of our sellers. When we stopped saying what’s a good idea, what moves any one of these 10 metrics and started saying, what are the fewest things we need to do to really accelerate gross merchandise sales, we came to a very different answer. We started doing much fewer things much better. That’s really been the key to our success.
There’s Been a Lot of Advances In Machine Learning
Change is hard. When running a marketplace we have access to a lot of data and insights that each individual seller won’t necessarily have. Our job is to really look after the good of the whole and be willing to make some decisions that sometimes, in the moment, may not feel obvious to every seller but really do lift all boats and make our sellers as a whole much better off. We’ve really focused at a high level on doing two things really well. One, make it much easier for people to find great products on Etsy. And two, once they’ve found those products to actually buy them.
With 62 million products for sale, picking for any given buyer the 20 or 30 that should be on page one of search results is a pretty interesting and pretty challenging task. There’s been a lot of advances in machine learning that take things that would have been literally impossible ten years ago and made those things much more possible today. The key is understanding what an item is with relatively little data and then being able to determine for each individual person how to personalize search results. We’ve made leaps and bounds in the science of search and machine learning. That’s more relevant at Etsy than almost anywhere else.
The mission of Etsy is incredible. As the nature of work changes creativity can’t be automated. The role we play for creators and makers being able to harness their creative passions and power and turn that into a way to earn a living for their families is a mission that I think is ever more important in this fast-changing economy.
There’s Been a Lot of Advances In Machine Learning, Says Etsy CEO Josh Silverman
“We’re making email marketing even smarter with a set of new AI capabilities getting released into Salesforce Marketing Cloud,” says Salesforce VP Armita Peymandoust. “One of them is Einstein Engagement Frequency. The other one is Einstein Send Time Optimization. We also have Einstein Content Tagging out and available today to our customers. Email is definitely not dead. Even the Millennials say that.”
AI-Powered Email Capabilities Released Into Salesforce Marketing Cloud
As we know email is still a really important channel. Over 64 percent of customers are still saying that they prefer email channels to all the others. What we’re doing is we’re making email marketing even smarter with a set of new AI capabilities getting released into Salesforce Marketing Cloud. One of them is Einstein Engagement Frequency. The other one is Einstein Send Time Optimization. We also have Einstein Content Tagging out and available today to our customers. Email is definitely not dead. Even the Millennials say that.
Einstein Engagement Frequency
With Einstein Engagement Frequency we’re trying to tell the marketer what’s the sweet range that they should keep on engaging with their customers. As marketers, we want to keep on engaging with our customers but we just don’t want to get to a point that we’re potentially annoying them. So we are telling them that this is the range that you should stay in.
Einstein Send Time Optimization
Now that the marketer knows what the frequency of engagement should be, with Einstein Send Time Optimization we’re also telling them what is the right time to send those messages. It’s really easy with a drag and drop of an activity into Journey Builder we make every message go out at the right time for the customers.
Einstein Content Tagging
Then with Einstein Content Tagging, we’re basically bringing image recognition the same set of AI capabilities that you’re familiar with for your customer based or consumer based products. This is where you upload photos and then they automatically get tagged. We are bringing that same technology to the hand of the marketer. Every image that’s getting uploaded into Content Builder gets automatically tagged so they can find it later and use it when they’re building their messages.
Transactional Messaging
We’re also releasing Transactional API’s for Emails and SMS. There are different types of emails out there. There’s the commercial one and there’s the transactional one. It allows the marketer to bring both of those two in an inter-marketing cloud and take advantage of Marketing Cloud to send those emails to have the same voice, the same brand voice, and also be able to see how those are performing all in one place.
Indiana Pacers Improved Customer Engagements By 20 Percent
These features are all relatively new. So we have pilot customers that have been taking advantage of them. We have one retailer that talked about Einstein Engagement Frequency. They had a hunch that they were over messaging customers but they couldn’t really put their finger on it. With Einstein Engagement Frequency we could show them visually exactly where they’re over engaging with their customers and let them take action on it. The platform automatically created lists so that they would not send messages to the ones that are getting too many email messages.
We’ve had a set of AI features in Marketing Cloud, specifically Einstein Engagement Scores, was one that the NBA’s Indiana Pacers is taking advantage of, to increase the engagement rate that they’re having with their fans. They got a 20 percent increase in engagements with their fans using that.
Customer Engagement Getting Even More Granular
We have a jam-packed roadmap for the next of the rest of the year as well. One of the things that I’m really excited about is Content Selection that’s coming out. Content Selection lets each of those messages that we’re creating be dynamically optimized for every customer that’s receiving them. Think of your email as a template that has different aspects or different selections in it that gets automatically replaced with what your customer cares about most and also what they have engaged with most and historically. It’s very engaging for every one of your customers.
The other one that I’m interested in (that is coming later) is bringing natural language processing to understanding your subject lines. What types of subject lines are resonating? Why is it that they’re resonating with your customers? It will give you an insight on them first and then also give you recommendations on how to improve your subject lines.
https://youtu.be/oaH_OrquEcE
New AI-Powered Email Capabilities Released Into Salesforce Marketing Cloud
According to Nuria Oliver, Data-Pop Alliance’s Chief Data Scientist, “There’s a massive opportunity for using big data to have positive social impact… But at the same time, we need to be aware of its limitations and be honest in terms of its performance.” Crime-Stopping artificial intelligence (AI), also known as Predictive Policing, is on the rise. In fact, at least 5 major cities use real-time facial recognition software, and over 50 police departments across the U.S.A. use forecasting software to predict future hotspots for minor crimes. Despite its growing use, is crime-stopping AI effective? You be the judge.
In a test of Amazon’s facial recognition software, 28 members of Congress were falsely identified as criminals. At large, African Americans are more likely to be included in facial recognition databases due to the over-policing of black communities. Retouching on Amazon’s facial recognition software test, only 20% of members of Congress are people of color – yet 39% of their false matches were people of color. Errors like this abound in facial recognition software, and it’s a well-documented phenomenon. Regardless of the documented shortcomings, AI facial recognition is being used across the world already.
Predictive policing is composed of several algorithms. Take PredPol for example, AI software developed by the LAPD back in 2008 to forecast the future places minor crimes such as theft and vandalism will take place. This technology finds its targets based on recent police reports and can target patrols down to a 500 square foot area. Outside of this, crime prediction software is built using pre-existing AI models and historical crime data. This information is then used to step up police presence in areas where crime is predicted to happen, based on the belief that crime begets more crime.
When AI is built upon historical crime data, predictions may become self-fulling and existing bias becomes a core component in its predictive algorithms. Moreover, results and enforcement ignore crimes that go unreported. In 2018, the Bureau of Justice Statistics said only 43% of violent crime and 34% of property crime was reported to the police. The primary reason for this is that people are less likely to report crimes they think will go unsolved. This further adds to the inaccuracy of artificial intelligence predicted crime as the predictions are based on incomplete and faulty information.
Andrew Ferguson, University of D.C. Law Professor & author of The Rise of Big Data Policing believes, “There’s a real danger, with any kind of data-driven policing, to forget that there are human beings on both sides of the equation.” Saying this, the tech powering crime prediction software such as Ford’s Self-Driving Police Car, the Knightscope K5, and even the Domain Awareness System must be taken into question. Especially since more police agencies are rumored to be using the tech without public disclosure.
AI isn’t new, but its role in criminal justice is. Is this technology able to be trusted to give us better community policing outcomes or should we stick to more traditional methods? Keep reading for more information on the spread of crime-stopping AI.
YouTube is warning that some users’ videos may be improperly flagged due to the company relying on artificial intelligence (AI) to moderate videos.
With more and more employees working from home during the coronavirus pandemic, YouTube is turning to AI and machine learning (ML) to make up for the shortage of human moderators. Unfortunately, AI and ML doesn’t always get it right and YouTube is warning that—in an attempt to keep violative content in check—some videos may be removed without actually violating policies.
“Our Community Guidelines enforcement today is based on a combination of people and technology: Machine learning helps detect potentially harmful content and then sends it to human reviewers for assessment,” the blog post reads. “As a result of the new measures we’re taking, we will temporarily start relying more on technology to help with some of the work normally done by reviewers. This means automated systems will start removing some content without human review, so we can continue to act quickly to remove violative content and protect our ecosystem, while we have workplace protections in place.”
Recognizing the potential inconvenience the situation will cause, YouTube will not be quick to issue “strikes” for removed content, and recommends users appeal any decision they believe was made in error.
“As we do this, users and creators may see increased video removals, including some videos that may not violate policies. We won’t issue strikes on this content except in cases where we have high confidence that it’s violative. If creators think that their content was removed in error, they can appeal the decision and our teams will take a look. However, note that our workforce precautions will also result in delayed appeal reviews. We’ll also be more cautious about what content gets promoted, including livestreams. In some cases, unreviewed content may not be available via search, on the homepage, or in recommendations.”
This is just another example of the pandemic’s far-reaching effects, as well as the increasing role AI and ML can play in a variety of situations.
Many companies are working to build authentic and trusted brands with consumers. This is especially true with pharmaceuticals, biotech, and med-tech companies. The CEO of Sparrho, Dr. Vivian Chan, says that their approach combines artificial intelligence and 400,000 Ph.D.’s to deliver scientific data to companies. This data helps companies back up their marketing messages which enables them to more effectively build that vital trust with their customers.
Dr. Vivian Chan, Sparrho CEO, recently discussed on CNBC their unique hybrid AI approach to helping companies use science and information to back up their brands messaging:
AI Enables Humans to Make Better-Informed Decisions
Artificial intelligence is really about algorithms and how we can use data that we collect to enable humans to make better-informed decisions. I not at all about having computers make decisions on behalf of humans. In a way, I think it’s machines that will be helping evolve the tasks and not actually replacing the human roles. Human roles themselves will be evolving also as the technology improves. This allows humans to have more headspace to be thinking about things that machines can’t do right now.
Machines can’t necessarily summarize a lot of pieces of contextual analysis very well yet to a 100 percent accuracy and humans are still better at making nonlinear connection points. For example, being able to say that this mathematical equation is super relevant to an agricultural problem. If we don’t have the tagging and reference and citations humans are still better at making those nonlinear new connection points than machines.
Humans are still good at coming up with the questions. If you actually pose the right question and you train the data and the algorithms you might actually get the right answer. However, you still need to have the humans to be thinking about what the questions are in order to ultimately get the answers.
It’s About Using AI as a Means to an End
I think the angle is really thinking about using AI as a means to an end and not just the end. Ultimately, this is a hybrid approach and various different people are calling it differently. Even MIT professors are calling it a hybrid approach. We’re calling it augmented intelligence. We need to come up with a good relationship between humans and machines. Marketing is about building relationships. It’s about building relationships between brands and consumers and now how do we build that relationship digitally?
Using Science to Build an Authenticated Brand
In this digital age, consumers are a lot more tech savvy but are also information savvy. They want to know what the is science behind certain things. Even if you’re talking about CPG, consumer packaged goods, what is the science behind a shampoo product right now when it claims 98 percent prevention of hair loss? What is the real science behind that and how do we actually bring that simplified science-oriented message to the consumer? How can consumers educate themselves and make informed decisions based on the products and thereby build a stronger brand relationship?
Ultimately what we’re trying to do at Sparrow is simplify science to build trust in brands. Especially for marketing departments and brands, it’s really allowing them to have the evidence-based science and the facts because building a very authenticated brand is what is meaningful to consumers. Research says that about 71 percent of consumers immediately reject content that looks like a sales pitch. Building a relationship and having an authenticated brand and content is super important in building that relationship between brand and consumers.
Sparrho Provides Content as a Service On Demand
We’re going even wider with that by providing what we call content as a service or relevant content on demand. We then integrate that into the digital platforms or the brands. We have what we call augmented intelligence with over 16 million pieces of content that is augmented by a network of more than 400,000 monthly active PhDs in a150 countries. They curate and summarize what’s actually happening in the latest of science.
We know that in about 60 percent of pharmaceuticals, biotech, and even med-tech companies, are spending more than $50 million per year just in content. Content has been the major driver for a lot of their marketing. In pharmaceuticals, they’re trying to really bring that relationship that they have offline to online. It’s at the heart of this digital transformation age that we are going through. This is really helping bring that relationship online by using the right engaging content. Our goal with Sparrow is to drive more engagement and ultimately more sales.
Google Cloud has announced the launch of Cloud AI Platform Pipelines, to help deploy machine learning (ML) pipelines.
“A machine learning workflow can involve many steps with dependencies on each other, from data preparation and analysis, to training, to evaluation, to deployment, and more,” writes Anusha Ramesh, Product Manager, TFX. “It’s hard to compose and track these processes in an ad-hoc manner—for example, in a set of notebooks or scripts—and things like auditing and reproducibility become increasingly problematic.”
Cloud AI Platform Pipelines is designed to help alleviate the challenges of creating an ML pipeline with all the necessary dependencies. The new platform provides a way to “deploy robust, repeatable machine learning pipelines along with monitoring, auditing, version tracking, and reproducibility, and delivers an enterprise-ready, easy to install, secure execution environment for your ML workflows.”
The new tool has two parts. The first is the enterprise-ready infrastructure the ML workflows will run on, and the second is the tools for creating the ML pipelines and components. Cloud AI Platform Pipelines has push-button installation in the Google Cloud Console and supports both the Kubeflow Pipelines SDK and the TFX SDK.
Google Cloud’s new tool is available as a beta and should be a welcome addition for customers eager to add artificial intelligence and ML workflows to their cloud environments.
Vermont Attorney General Donovan has filed a lawsuit against Clearview AI, claiming the facial recognition firm has broken multiple state laws.
Clearview AI has scraped millions of websites to amass a database of some 3 billion photos, on which it uses artificial intelligence to analyze. The company then makes its software available to law enforcement agencies. Despite its claims of being responsible with the data it collects, recent revelations have shown that nothing could be further from the truth.
Clearview was caught using its software to monitor when police officers spoke with journalists and discourage them from doing so. The company’s plans to expand and form partnerships with authoritarian regimes was leaked, only to have its client list stolen, showing such expansion plans were already underway. Clearview also has claimed it only makes its software available to law enforcement and security personnel when, in fact, a wide array of investors and other individuals have had access and used the app for their own purposes.
Now Vermont’s AG is taking measures to call the company to account. The complain, “alleges violations of the Vermont Consumer Protection Act and the new Data Broker Law. Along with the complaint, the State filed a motion for preliminary injunction, asking the Court to order Clearview AI to immediately stop collecting or storing Vermonters’ photos and facial recognition data.”
AG Donovan did not mince any words in denouncing the company’s practices.
“I am disturbed by this practice, particularly the practice of collecting and selling children’s facial recognition data,” Attorney General Donovan said. “This practice is unscrupulous, unethical, and contrary to public policy. I will continue to fight for the privacy of Vermonters, particularly our most vulnerable.”
It’s safe to say individuals around the country will be rooting for AG Donovan.
Schneider Electric has announced the release of the Uniflair Rack Mounted Cooling Solution, specifically aimed at edge computing and micro data centers.
The solution is aimed at freeing up floor space by using the bottom of an IT rack. This makes it ideal for applications, such as on-premise processing, where space is at a premium.
“Simply put, our new vendor-neutral, rack mounted cooling solution is right-sized for edge micro data centers and provides the right answer for cooling today’s critical edge technology,” said Maurizio Frizziero, Director of Cooling, Schneider Electric. “It offers more cooling in less space and simplifies management and maintenance, making it ideal for industries like retail, finance, health care, light manufacturing, and education.”
As 5G technology boosts edge computing, on or near-premise data processing will become far more important for a variety of technologies, such as artificial intelligence, augmented reality, virtual reality, self-driving cars and more. Solutions such as Uniflair will become an increasingly critical component, helping ensure the success of those technologies.
Verizon Business and the Pacific Northwest National Laboratory (PNNL) are teaming up to deliver 5G applications.
The PNNL tackles some of the world’s biggest challenges, including energy efficiency, scientific discovery and national security. To aid in that goal, Verizon will be deploying its 5G Ultra Wideband at the PNNL’s Richmond, Washington facility. Together, the organizations will develop 5G applications for use in everything, ranging from first responders to chemistry to earth sciences research.
Verizon’s 5G Ultra Wideband promises speeds measured in gigabits rather than megabits, along with sub-millisecond lag. That performance will open a world of new possibilities for PNNL, as it researches artificial intelligence, machine learning, AR/VR and more.
“With Verizon, we plan to explore how cybersecurity will underpin 5G for critical infrastructure and how 5G will drive transformation in the protection of endpoint devices, advancement of artificial intelligence, the science behind autonomous systems and related internet of things applications,” said Scott Godwin, general manager of Corporate Partnerships & Alliances at PNNL. “This partnership fits squarely with PNNL’s commitment to explore the capability of new science and technology to further safety and security worldwide.”
“Our 5G Ultra Wideband network is built to support transformational innovations and solutions across all industries,” said Tami Erwin, CEO of Verizon Business. “There’s no doubt 5G’s increased data bandwidth and super-low lag will help play a critical role in evolving response connectivity and mission operations for first responders. We’ve seen exciting use cases come out of our 5G First Responder Lab and are thrilled to see the new applications that will arise from our work with PNNL.”
DocuSign is moving into AI with the acquisition of Seal Software, maker of AI-driven contract analysis, for $188 million in cash.
DocuSign is one of the leading electronic contract platforms, providing a way for companies to share, organize and sign electronic documents. DocuSign already resells Seal’s software as part of its DocuSign Agreement Cloud. The acquisition will drive further integration between the two platforms.
“As the Agreement Cloud company, DocuSign is about digitally transforming the very foundation of doing business: agreements and agreement processes,” said Scott Olrich, DocuSign’s chief operating officer. “We believe that AI will play a vital role in this transformation. And by integrating Seal into DocuSign, we can benefit from its deep technology expertise and its broad experience applying AI to agreements.”
According to the statement, “Seal is recognized as one of the pioneers in AI-driven contract analytics. Its technology can rapidly search large collections of agreements by legal concepts (rather than just by keywords); automatically extract and compare critical clauses and terms side-by-side; quickly identify areas of risk and opportunity; and deliver actionable insights that help solve legal and business challenges.”
DocuSign will continue to sell Seal’s software, in addition to integrating it with DocuSign CLM.
“For DocuSign customers, these capabilities will mean faster, more efficient agreement processes. Seal customers will in turn benefit from deeper access to the full capability of the DocuSign Agreement Cloud—especially document generation and advanced workflows.”
DocuSign’s acquisition of Seal Software illustrates the wide-ranging industries AI continues to impact.
AI startup Celonis has announced that President of Salesforce International, Miguel Milano, is joining the company as co-owner and Chief Revenue Officer.
Milano “led Salesforce’s international businesses across Asia-Pacific, Europe, the Middle East, Africa and Latin America.” His departure comes during a difficult week for Salesforce. Co-CEO Keith Block abruptly resigned from his role. The move caught the industry off guard, leading CEO Marc Benioff to take measures to reassure investors that it was business-as-usual for the company.
Now Salesforce is losing another top executive, one who has an outstanding reputation in the industry. Milano joins a company that touts itself as “the market leader in AI-enhanced Process Mining and Process Excellence software,” with Siemens, 3M, Airbus and Vodafone among its list of clients.
“Miguel is an outstanding leader with a phenomenal track record of building winning teams that deliver value for customers,” said Alexander Rinke, Co-Founder and Co-CEO. “We are honored that he chose Celonis as his next endeavor and that he is investing in the company as an owner. He shares our values and ambition to delight our customers and make a positive impact on our stakeholders. We are thrilled to have him on board.”
Milano struck an optimistic tone about his move to Celonis, saying: “I look forward to driving exponential growth at Celonis, focused on supporting customers become more efficient and sustainable in its operations and supply chains, and more customer-centric in its front-end processes. Process Mining is the foundation for a new, frictionless way of working which delivers significant business value to organizations in all industries. Salesforce is a once-in-a-generation company and I am convinced Celonis is well on its way to becoming one too. It’s incredibly exciting to join a hyper-growth company that is so innovative and groundbreaking and at the same time remains humble, customer-oriented and focused on making the world more sustainable.”
Pony.ai has announced that it has secured $400 million in funding from Toyota to help develop its autonomous driving tech.
Pony.ai claims it is “developing the safest and most reliable autonomous driving technology globally. Having accumulated millions of kilometers in autonomous road testing in the most complex scenarios, we have a solid foundation to deliver autonomous driving systems at scale.”
The investment comes as virtually every major automaker, and numerous technology companies, are pursing autonomous driving tech. So far, Toyota has kept most of its plans under wraps, especially compared to some of its rivals.
Based in both the U.S. and China, Pony.ai’s technology is designed to meet the challenges of roads and driving conditions in vastly different and unique circumstances. A key element to that approach is the company’s Perception module.
“Our Perception module combines the strengths of a heuristic approach and deep learning models to boost performance, while ensuring the safety and operational redundancy of our vehicles,” says the company’s website. “Performance capability is further enhanced by our multi-sensor fusion technology, which intelligently leverages the most reliable sensor data depending on different environmental or driving scenarios.”
Pony.ai’s technology should go a long way toward helping Toyota meet its autonomous driving goals—whatever they may be.
A hundred years ago, hemp was farmed all over the United States by traditional means. Cannabis prohibition shut all those operations down, and while the 2018 Farm Bill legalized the production of hemp across the nation once again, its uses have shifted significantly. Whereas a hundred years ago hemp was mainly used for rope and fiber, these days it’s increasingly used for food and medicine, which means it needs to be as pure as possible. The best way to achieve that is through the use of hydroponics in a controlled, indoor environment, and that requires the help of tech like robotics and artificial intelligence.
Because hemp plants are bioaccumulators, that means that any pollution the plants come in contact with in the soil, air, or water will be trapped within the plants. The good news is that hemp plants generally don’t need pesticides or herbicides to grow well under normal growing conditions, so those hemp plants that are destined for fiber applications can be easily grown outdoors. But because of the risk of contamination from pollution, coupled with the risk of being cross-pollinated with the higher-THC variety of cannabis, hemp plants destined for medicinal or food uses are generally being grown indoors.
Cross contamination is also a concern for the legal marijuana industry. The illicit marijuana industry has benefited from prohibition because a lack of other plants that could cross-pollinate meant that growing illicit marijuana outdoors didn’t threaten the harvest. But because of two distinctly different but similar industries slowly becoming legalized across the nation, growing either outdoors means a risk of cross-pollination, which could render marijuana plants too weak for their target market and hemp plants too strong for thiers. By 2025, legal marijuana sales in the United States are expected to reach $23 billion. Protecting their plants from the threat of cross-contamination also means that the legal marijuana industry is going to increasingly rely on hydroponics, robotics, and artificial intelligence to produce a superior quality product.
Both types of plants are susceptible to mold and rot when humidity and ventilation are not well-controlled. This is where robotics and AI come in. AI can monitor the moisture levels in the air, the air flow, and photograph plants and scan for signs of mold or rot and then tell robotics how to respond. The robotics can be set up to hydrate or desiccate, circulate air, increase water or nutrients, and more. Systems like Farmbot can be used to grow a variety of plants and use robotics and AI to collect data and learn what plants need over time.
Vertical farming is another technology that is being used to make the production of cannabis plants as environmentally-friendly as possible. Systems like Agrify can be set up to be six levels high, and its integrated systems monitor growing conditions and make necessary adjustments. This makes the production both less reliant on human intervention and also more efficient of resources. Learn more about the tech being used to grow cannabis from the infographic below!
Elon Musk, a long-time critic of AI, has come out in favor of government regulation of AI development, including at his own company.
While many working on AI believe it is the key to solving countless world problems, there are just as many who are convinced the technology will create far more problems than it solves, perhaps even bringing about the downfall of humanity. Musk has tended to be in the latter camp, even being quoted as saying “I have exposure to the most cutting-edge AI and I think people should be really concerned about it. I keep sounding the alarm bell but until people see robots going down the street killing people, they don’t know how to react because it seems so ethereal.”
That concern didn’t stop Musk from co-founding OpenAI, dedicated to the ongoing development of the technology, however. In fact, Musk’s concerns were one of the driving motivations, as he believed the technology needed responsible development, as opposed to being left in the hands of just a few companies—such as Google and Facebook—who have poor track records protecting user privacy.
Now, in response to a piece by Karen Hao in the MIT Technology Review that covers “OpenAI’s bid to save the world,” Elon Musk has tweeted his support of AI regulation.
All orgs developing advanced AI should be regulated, including Tesla
Ericsson researchers have set a new record for mmWave 5G, hitting 4.3Gbps download speeds.
mmWave is the fastest variety of 5G, and offers revolutionary speeds compared to 4G. Speeds are so fast that experts have long said it could disrupt entire industries, making things possible that could never be done with previous technology. Artificial intelligence, autonomous cars, mobile gaming, virtual and augmented reality are just a few of the industries 5G stands to have a major impact on.
Ericsson’s researchers have just displayed some of that promise, achieving a whopping 4.3Gbps downlink with mmWave 5G.
“This is a fantastic achievement,” said Per Narvinger, Head of Product Area Networks. “To put 4.3Gbps in context, that is the equivalent of downloading one hour of ultra-high-definition, or 4K, content from a streaming service in just 14 seconds. Ericsson is taking the next steps in ensuring service providers can deliver the best capacity and data rates over millimeter wave 5G. The 8CC aggregation solution we have successfully tested will enable not only higher speeds but also large-scale 5G deployments and new business opportunities.”
In the U.S., T-Mobile, Verizon and AT&T have all deployed mmWave 5G in parts of the country, although rollout is slow as a result of the frequency’s extremely short range. This has led T-Mobile and AT&T to complement their mmWave service with low-band 5G, which has far better range and coverage but offers speeds similar to 4G.
Ericsson’s announcement also lends weight to CEO Borje Ekholm’s recent comments that his company is at the forefront of 5G, both in terms of technology and scalability. In that interview, he said: “I find it’s a bit difficult to say that we’re behind when I see no one ahead of us.”
Oracle has announced the launch of its Oracle Cloud Data Science Platform, aiming to help enterprises take advantage of AI and machine learning.
The new platform is an acknowledgement of the fact that few organizations today benefit from data science and machine learning to the extent possible. In many cases, this is because they lack the tools to take advantage of the data at their disposal. The new platform will enable enterprise customers to “collaboratively build, train, manage and deploy machine learning models to increase the success of data science projects.”
The Cloud Data Science Platform is designed specifically with data science teams and scientists in mind, and includes the tools they need to streamline their processes.
“Effective machine learning models are the foundation of successful data science projects, but the volume and variety of data facing enterprises can stall these initiatives before they ever get off the ground,” said Greg Pavlik, senior vice president product development, Oracle Data and AI Services. “With Oracle Cloud Infrastructure Data Science, we’re improving the productivity of individual data scientists by automating their entire workflow and adding strong team support for collaboration to help ensure that data science projects deliver real value to businesses.”
With computer and information research scientists being in high demand, with tremendous future growth opportunities, tools like Oracle’s latest will become ever more important.
The first drug developed using artificial intelligence (AI) is moving forward with human trials, ushering in a new era of medical research and development.
The drug, DSP-1181, is designed to treat obsessive compulsive disorder (OCD) and was created in a joint venture between the UK’s Exscientia and Japan’s Sumitomo Dainippon Pharma. Most significantly, while most drugs take five years to go to trial, DSP-1181 made it in just 12 months.
AI was directly responsible for the short development time, according to Exscienta chief executive, Professor Andrew Hopkins. He said the new drug was created using algorithms that AI was able to sift much faster than a human, comparing those algorithms of potential compounds to a database of parameters.
“There are billions of decisions needed to find the right molecules and it is a huge decision to precisely engineer a drug,” Professor Hopkins told the BBC.
“But the beauty of the algorithm is that they are agnostic, so can be applied to any disease,” he added.
While AI is increasingly being used in medicine, and specifically in diagnostic medicine, this is the first time a drug it was heavily involved in creating has made it to clinical trials. Needless to say, it won’t be the last.
