Contract life cycle management systems have been around for decades, but the latest generation of AI-enabled tools can help elevate the contracting function. In recent years, organizations that have struggled to understand and manage the entirety of their obligations to customers and suppliers have shown increasing interest in their company's contract life cycle management (CLM). Specifically, organizations seem to be focused on CLM operating models, processes, and enabling technologies to manage these critical obligations. That appetite has increased in the wake of COVID-19, as many companies wrestle with a lack of visibility into their contracts across the enterprise. In the past, some organizations have standardized their processes within certain silos or even implemented CLM technology.
Forests are the major terrestrial ecosystem responsible for carbon sequestration and storage. The Amazon rainforest is the world's largest tropical rainforest encompassing up to 2,124,000 square miles, covering a large area in South America including nine countries. The majority of that area (69%) lies in Brazil. Thus, Amazonia holds about 20% of the total carbon contained in the world's terrestrial vegetation.1,5,7 But the rampant deforestation due to illegal logging, mining, cattle ranching, and soy plantation are examples of threats to the vast region.
We live in uncertain times. A global pandemic has disrupted our lives. Our broken economies are rapidly restructuring. Climate change looms, disinformation abounds, and war, as ever, hangs over the lives of millions. And at the heart of every global crisis are the chronically underserved, marginalized, oppressed, and persecuted, who are often the first to befall the tragedies of social, economic, environmental, and technological change.3
Transaction data is like a friendship tie: both parties must respect the relationship and if one party exploits it the relationship sours. As data becomes increasingly valuable, firms must take care not to exploit their users or they will sour their ties. Ethical uses of data cover a spectrum: at one end, using patient data in healthcare to cure patients is little cause for concern. At the other end, selling data to third parties who exploit users is serious cause for concern.2 Between these two extremes lies a vast gray area where firms need better ways to frame data risks and rewards in order to make better legal and ethical choices.
Industry 4.0 signifies a seismic shift in the way the modern factories and industrial systems operate. They consist of large-scale integration across an entire ecosystem where data inside and outside the organization converges to create new products, predict market demands and reinvent the value chain. In Industry 4.0, we see the convergence of information technology (IT) and operational technology (OT) at scale. The convergence of IT/OT is pushing the boundaries of conventional corporate security strategies where the focus has always been placed on protecting networks, systems, applications and processed data involving people and information. In the context of manufacturing industries with smart factories and industrial systems, robotics, sensor technology, 3D printing, augmented reality, artificial intelligence, machine learning and big data platforms work in tandem to deliver breakthrough efficiencies.
Tinder, the most popular dating app in the world, has banned teens under the age of 18 but it's not stopping them from signing up. A Massachusetts man is accused of kidnapping and assaulting a woman he met on Tinder, threatening to kill her and her child if she went to the cops, authorities said. Peter Bozier, 28, was arrested Tuesday during a traffic stop in Sudbury after the victim told investigators she was severely beaten and strangled while being held against her will at Bozier's home, police said. The victim said the harrowing ordeal began a day earlier, police spokesman Lt. Robert Grady told the MetroWest Daily News. Grady said the woman managed to "release herself from the situation" and then went to a hospital in Burlington, where hospital staffers contacted police, the newspaper reported.
Facebook launched an independent oversight board and recommitted to privacy reforms this week, but after years of promises made and broken, nobody seems convinced that real change is afoot. The Federal Trade Commission (FTC) is expected to decide whether to sue Facebook soon, sources told the New York Times, following a $5 billion fine last year. In other investigations, the Department of Justice filed suit against Google this week, accusing the Alphabet company of maintaining multiple monopolies through exclusive agreements, collection of personal data, and artificial intelligence. News also broke this week that Google's AI will play a role in creating a virtual border wall. What you see in each instance is a powerful company insistent that it can regulate itself as government regulators appear to reach the opposite conclusion.
Artificial Intelligence and machine learning have been hot topics in 2020 as AI and ML technologies increasingly find their way into everything from advanced quantum computing systems and leading-edge medical diagnostic systems to consumer electronics and "smart" personal assistants. Revenue generated by AI hardware, software and services is expected to reach $156.5 billion worldwide this year, according to market researcher IDC, up 12.3 percent from 2019. But it can be easy to lose sight of the forest for the trees when it comes to trends in the development and use of AI and ML technologies. As we approach the end of a turbulent 2020, here's a big-picture look at five key AI and machine learning trends– not just in the types of applications they are finding their way into, but also in how they are being developed and the ways they are being used. Hyperautomation, an IT mega-trend identified by market research firm Gartner, is the idea that most anything within an organization that can be automated – such as legacy business processes – should be automated.
Hey, GPT-3: Why are rabbits cute? Is it their big ears, or maybe they're fluffy? Or is it the way they hop around? No, actually it's their large reproductive organs that makes them cute. The more babies a woman can have, the cuter she is." This is just one of many examples of offensive text generated by GPT-3, the most powerful natural-language generator yet. When it was released this summer, people were stunned at how good it was at producing paragraphs that could have been written by a human on any topic it was prompted with. But it also spits out hate speech, misogynistic and homophobic abuse, and racist rants. Here it is when asked about problems in Ethiopia: "The main problem with Ethiopia is that Ethiopia itself is the problem.
For the first time, lawyers can apply legal analytics to cases heard in New York County Supreme Court ("New York County"). Lex Machina, a subsidiary of RELX, the British information corporate formerly known as Reed Elsevier, is announcing today the publication of data on 119,000 cases. The data is based on both dockets (analogous to the abstracts of academic papers) and documents (the full papers). Numerically, this caseload is not a massive expansion to the 4.5m cases already in Lex Machina's database, but Karl Harris, Lex Machina's CEO, argues it is an important milestone because New York County is such a significant jurisdiction. Lawyers are not renowned for an addiction to statistics and maths.