Diversity of skills, perspectives, experiences and geographies has played a key role in our digital transformation. At Levi Strauss & Co., our growing strategy and AI team doesn’t include solely data and machine learning scientists and engineers. We recently tapped employees from across the organization around the world and deliberately set out to train people with no previous experience in coding or statistics. We took people in retail operations, distribution centers and warehouses, and design and planning and put them through our first-ever machine learning bootcamp, building on their expert retail skills and supercharging them with coding and statistics. We did not limit the required backgrounds; we simply looked for people who were curious problem solvers, analytical by nature and persistent to look for various ways of approaching business issues. The combination of existing expert retail skills and added machine learning knowledge meant employees who graduated from the program now have meaningful new perspectives on top of their business value.
The global pandemic has highlighted a need for more flexible customer service, using digital channels, as well as the possibility of organisations delivering service without being tied down to a particular location. Both factors have driven increased adoption of hyper-automation, and have led to more differentiation in customer service joining the biggest trends in the space. According to Luis Huerta, vice-president and intelligent automation practice head, Europe at Firstsource, “as fixed-schedule, routine, processes and tasks are automated in the back-office, the need for staff to be tied to a specific location diminishes. Furthermore, with hyper-automation, the role of human colleagues switches from hands-on task execution to managing and monitoring bots, and dealing with complex business exceptions. ... As end customers are increasingly able to leverage automated channels to solve their needs, the pressure on support staff reduces and we give front-line colleagues an ability to focus on complex enquiries where a human touch is critical.
Blockchain technology offers a way to make life and work easier, regardless of the industry or class, and the ride-sharing industry is one a lot of disruptors and companies in the blockchain space are looking to become major players in. There have been a lot of bold claims about giving drivers and users more freedom through the use of decentralized technology such as that of the blockchain. One of the companies that made this claim is Drife. Drife is a decentralized ride-sharing and peer-to-peer ride-sharing platform that was started with the intent of empowering the drivers and riders within its ecosystem. The app is built on the Aeternity blockchain and its business model is built on taking zero commission from its drivers. Drife will instead charge drivers an annual fee on its platform to access the app. “We believe when there’s a driver who spends 14 to 16 hours behind the wheel, he deserves to take back all the income to his home,” said Sheikh. ... While Uber, Lyft and others were formed with good intentions, they have become centralized, continuously paying their drivers less and charging their riders more.
Researchers have long debated whether it would be worth the effort for scammers to train machine learning algorithms that could then generate compelling phishing messages. Mass phishing messages are simple and formulaic, after all, and are already highly effective. Highly targeted and tailored “spearphishing” messages are more labor intensive to compose, though. That's where NLP may come in surprisingly handy. At the Black Hat and Defcon security conferences in Las Vegas this week, a team from Singapore's Government Technology Agency presented a recent experiment in which they sent targeted phishing emails they crafted themselves and others generated by an AI-as-a-service platform to 200 of their colleagues. Both messages contained links that were not actually malicious but simply reported back clickthrough rates to the researchers. They were surprised to find that more people clicked the links in the AI-generated messages than the human-written ones—by a significant margin. “Researchers have pointed out that AI requires some level of expertise. It takes millions of dollars to train a really good model,” says Eugene Lim
Simply stated, a data mesh invests ownership of data in the people who create it. They’re responsible for ensuring quality and relevance and for exposing data to others in the organization who might want to use it. A consistent and organization-wide set of definitions and governance standards ensures consistency, and an overarching metadata layer lets others find what they need. “Data mesh is the concept of data-aligned data products,” Dehghani said in a video introduction. “Find the analytical data each part of the organization can share.” Dehghani lists eight attributes of a data mesh. Elements must be discoverable, understandable, addressable, secure, interoperable, trustworthy and natively accessible and they must have value on their own. The concept of decentralized data management is nothing new. Distributed databases rode the coattails of the client/server craze in the 1990s. Part of the appeal of the Hadoop software library of a decade ago was that processing was distributed to where data lived.
The battle between an attacker and the defenders is exactly the reason where the human factor comes into play and AI helps those defenders to focus and make decisions that optimize their time and skills. What we're seeing today is basic technology that’s designed for very specific attacks. It's only in 0.1 percent of attacks that very sophisticated technology is being used. There are millions of attacks every day, so you'll see advanced techniques; whereas, nine million other attacks are happening that are just super rudimentary, garden variety ransomware attacks and viruses. The latter are the mass of the attacks, and they're also the mass of the damage. If you're a nuclear reactor, then somebody's going to do massive harm, but if you're an average SMB, then you're a lot more susceptible to those garden variety attacks that we call drive-bys. Those machines aren't cutting edge and those attacks aren’t either. They're just the common things that have been learned over the past few years. However, with the forefront of attacks and premium ATPs, it'll be a battle of wits between the advanced technology versus their technology.
It's important to remember that quantum computers aren't just faster computers, but harbingers of an entirely new type of computation. “If realized in the best possible way imaginable, they would fundamentally change the world as we know it,” says Tom Halverson, a staff quantum scientist on the quantum computing team at management and information technology consulting firm Booz Allen Hamilton. “Because of this, many powerful forces are positioning themselves to be ‘the first,’” he states. “When the quantum computing revolution happens, it will happen quickly.” Quantum computing is already real, but it's simply not yet practical, observes Mario Milicevic, an IEEE member and a staff communication systems engineer at MaxLinear, a broadband communications semiconductor products firm. He notes that IT leaders will need to understand whether a quantum computer is the appropriate tool for the type of problem their organization is trying to solve. “For the majority of problems, classical computers will actually outperform quantum computers and do so at a much lower cost,” Milicevic states.
A quantum computer, integrated with our new neural-network estimator, combines the advantages of the two approaches. A quantum computer, integrated with our new neural-network estimator, combines the advantages of the two approaches. While a quantum circuit of choice is being executed, we exploit the power of quantum computers to interfere states over an exponentially-growing Hilbert space. After the quantum interference process has worked its course, we obtain a finite collection of measurements. Then a classical tool—the neural network—can use this limited amount of data to still efficiently represent partial information of a quantum state, such as its simulated energy. This handing of data from a quantum processor to a classical network leaves us with the big question: How good are neural networks at capturing the quantum correlations of a finite measurement dataset, generated sampling molecular wave functions? To answer this question, we had to think about how neural network could emulate fermionic matter. Neural networks had been used so far for the simulation of spin lattice and continuous-space problems.
The truth is that VR is not far off becoming an essential tool for helping businesses to become smarter and more efficient in the way they train staff. For example, vocational training provider Mimbus uses VR training for a range of skills including carpentry, construction, decorating, electrical engineering, and food processing. Working with HP VR hardware, the immersive nature of VR removes the pressures of getting things wrong in real life and increases confidence when it comes to performing skills on the job. This solution can help businesses significantly cut training costs. VR can also help businesses to communicate with clients and design new products and services. In fact, in a sales and marketing capacity, studies have shown that customers have a 25% higher level of focus when in a virtual space, showing that VR is a great way to capture customers’ attention. Alongside biosensors and AI, VR could be used in the future to test how drivers feel about a new car interior before it has been built, or improve the outcome of virtual meetings and collaboration by capturing the nonverbal cues of participants.
Expert systems were proving to be brittle and costly, setting the stage for disappointment, but at the same time learning-based AI was rising to prominence, and many researchers began to flock to this area. Their focus on machine learning included neural networks, as well as a wide variety of other algorithms and models like support vector machines, clustering algorithms, and regression models. The turning over of the 1980s into the 1990s is regarded by some as the second AI winter, and indeed hundreds of AI companies and divisions shut down during this time. Many of these companies were engaged in building what was at the time high-performance computing (HPC), and their closing down was indicative of the important role Moore’s law would play in AI progress. Deep Blue, the chess champion system developed by IBM in the later 1990s, wasn’t powered by a better expert system, but rather a compute-enabled alpha-beta search. Why pay a premium for a specialized Lisp machine when you can get the same performance from a consumer desktop?
Quote for the day:
"Leaders must be good listeners. It_s rule number one, and it_s the most powerful thing they can do to build trusted relationships." -- Lee Ellis