In the seemingly endless quest to reconstruct human perception, the field that has become known as computer vision, deep learning has so far yielded the most favorable results. Convolutional neural networks (CNN), an architecture often used in computer vision deep learning algorithms, are accomplishing tasks that were extremely difficult with traditional software. However, comparing neural networks to the human perception remains a challenge. And this is partly because we still have a lot to learn about the human vision system and the human brain in general. The complex workings of deep learning systems also compound the problem. Deep neural networks work in very complicated ways that often confound their own creators. In recent years, a body of research has tried to evaluate the inner workings of neural networks and their robustness in handling real-world situations. ... The researchers note that the human visual system is naturally pre-trained on large amounts of abstract visual reasoning tasks. This makes it unfair to test the deep learning model on a low-data regime, and it is almost impossible to draw solid conclusions about differences in the internal information processing of humans and AI.
In terms of mindset, your perspective is important. One of my colleagues (an especially responsive leader herself) says her grandmother has a gift for making each grandchild feel valued and unique. Great leadership is like this as well. While no one should play favorites, it’s powerful for each team member to feel they matter and know you appreciate them and their contribution. When you give people responsibility and trust them to do good work, you won’t have to be as involved in the work they’re doing. Your time will be spent coaching, developing and making decisions where your perspective or position are most critical. You should set guardrails—for example spending more than a certain amount of money or which key topics require your input or decision-making—but within those boundaries, set people free. By not being too deeply in the details, you’ll have more time to be accessible where you’re needed most. Another mindset to help you be more responsive is to know your people well. When you have a good sense of what motivates each employee and what their unique needs are, you’re able to tune your messages. You’ll be more responsive when you’re able to meet employees where they are and provide the information or direction they need most.
Unlike a robot siege that might damage property, the harm caused by these deep fakes was the erosion of trust in people and society itself. The threat of A.I. may seem to be forever stuck in the future — after all, how can A.I. harm us when my Alexa can't even correctly give a weather report? — but Shane Johnson, Director of the Dawes Centre for Future Crimes at UCL which funded the study, explains that these threats will only continue to grow in sophistication and entanglement with our daily lives. "We live in an ever-changing world which creates new opportunities - good and bad," Johnson warns. "As such, it is imperative that we anticipate future crime threats so that policymakers and other stakeholders with the competency to act can do so before new 'crime harvests' occur." While the authors concede that the judgments made in this study are inherently speculative in nature and influenced by our current political and technical landscape, they argue that the future of these technologies cannot be removed for those environments either. HOW DID THEY DO IT — In order to make these futuristic judgments, the researchers gathered a team of 14 academics in related fields, seven experts from the private sector, and 10 experts from the public sector.
One thing a consumer prefers the most would be, multiple services across one platform. Many Fintech brands have already rolled out this process of offering multiple services across one app, but the increase in offerings of robust solutions through powerful API integrations will add on. In the coming days, consumers who need banking services are likely to turn to those financial players, who can offer convenience and ease of transactions that is entirely safe and secure. To address these consumer needs, banks cannot do much, but technology can help a lot in digitalizing consumer demand. Blockchain and Big Data are two technologies in full swing, but they are also two complementary technologies. According to experts, brands adopting burgeoning blockchain technology will benefit the most. Financial services will be able to reduce fraudulent activities, phishing attacks and ensure secure payments. One of the other things that Fintech needs to bring their attention to is—Artificial Intelligence, Machine Learning and Data Analytics. As all these can help financial services in addressing their key challenges like cost reduction and scrutinize risky transactions.
The common denominator of these companies is their definition as cybersecurity firms. "The law doesn't allow companies or individuals to get involved with offensive cyber," according to Dr. Harel Menashri, head of the cyber department at the Holon Institute of Technology, who was a co-founder of the Shin Bet Cyber Warfare Unit. "The Israeli cyber industry has made itself a good name regarding advanced capabilities ... One of the greatest advantages of the Israeli culture is the ability to develop and move around things very quickly. Even if I didn't serve in the same unit with someone who I'm interested in, I'll probably know someone who did," Menashri added. "Israelis gain their technological knowledge during their military service through units like 8200 and the cyber units of Shin Bet and the Mossad. That knowledge is a weapon, and today, quite a few IDF veterans from intelligence units move abroad and share their knowledge with foreign parties." Menshari gave the example of a group of young Israelis who had graduated the IDF's elite Unit 8200 and a few months ago decided to go and work for the UAE-based intelligence firm Dark Matter after being tempted by large sums of money.
A great place to begin is your component library. Identify which components are used the most often and which underlying components underpin other functions. For example, make sure buttons, inputs and links have accessible focus and hover states. It’s a lucrative, efficient way of scaling accessibility fixes because once you make one fix, you’ll see it propagate throughout the organization wherever that component is used. There are a few key factors to be aware of at this stage. First, create a clear plan for who can make changes and how you’re testing components to ensure accessibility features are not unintentionally removed. Second, your work doesn’t end after creating accessible components. In the UI, individual components are put together like puzzle pieces, and just because each piece is accessible doesn’t mean the entire UI will be. Since the UI involves multiple components talking to each other, you’ll need to ensure that the experience is usable and accessible as a whole. The goal is to ensure every existing and new component in a library is accessible by default. This way, when developers pull features into their work, they’ll know with certainty it’s designed to be accessible. Get it right once, and you get it right everywhere.
A priority for cities in the years to come will be reducing air pollution levels. This is already a major concern – nine in ten people breathe polluted air resulting in seven million deaths every year, according to the World Health Organisation. As city populations and traffic volumes boom, the role of smart technology in tackling pollution will be crucial. While data on emissions and congestion has been available for some time, only recently have we been able to build a full picture of its reach and harm. Fusing data from various sources can reveal new insights to be used to manage energy use and minimise pollution. For example, IoT sensor technology can intelligently detect when there is little or even no pedestrian or road traffic, dimming streetlights autonomously and saving energy. By crunching vehicle rates in real time, as well as pressure, temperature and humidity, air quality levels can be accurately predicted and mapped. This provides the insight to proactively adapt traffic controls and mitigate harm. As always, the smart move is to analyse and adopt best practices from other cities and nations. Singapore, for example, is generally considered to be the global smart city leader, much due to significant government investments in digital innovation and connected technologies.
IoT and wearable devices are ideally placed to transform the management of both preventable and chronic diseases and represent a big opportunity for digital to disrupt the industry. Data on human health can now be collated to a level and scale that was never before possible, while innovations in machine learning and adaptive algorithms provide credible predictors for the risk of diseases. Such data gives us actionable insight, empowering us to make small but significant changes to lifestyle habits so we may work towards living a longer, healthier life. The opportunity, however, does not come without challenges, and two of the biggest obstacles that must be negotiated lie in the budgetary and the clinical. On the financial side, the system either lives or dies depending on whether doctors have the additional time and expertise to interpret and implement a treatment plan based on the assessment of vast reams of data. On the clinical side, non-medically graded user-generated data makes it challenging for a doctor to include this within the overall treatment decision-making process. The strength of AI and machine learning, of course, is that it can cope with large amounts of data and find statistical correlations where they exist.
Open Service Mesh builds on SMI, which is expressly not a service mesh implementation, but rather a set of standard API specifications designed within CNCF. If followed, the specs allow service mesh interoperability across multiple types of networks, including other service meshes, and public, private and hybrid clouds. The service mesh layer will be a key component of broadly accessible, real-world multi-cloud container portability as mainstream enterprise cloud-native applications advance, Pullen said. “Service mesh should help that, theoretically, especially if there’s standardization of it, but it’s going to require an interesting rework to make any Docker container compatible with any container cluster,” he said. “It’s more than putting something in Docker, it’s about that ability to route services in a somewhat decoupled way.” Simplicity and ease of use was also a point of emphasis in Microsoft’s OSM rollout, which analysts said seemed to target another common complaint about operational complexity among early adopters of Istio. OSM, by contrast, will build in some services that have been complex for service mesh early adopters to set up themselves, such as mutual TLS authentication.
Agile management began as a work of passion. It was born of a fierce desire felt by disgruntled software developers to set things right. Their Agile Manifesto (2001) not only succeeded in its modest goal of "uncovering better ways of developing software.” It had the unintended consequence of generating a candidate as the paradigm for 2020 management generally. Thus, Agile management began with exploring more nimble processes for one team, then several teams, then many teams and then the whole organization. It set in train the emergence of firms like Amazon and Google that not only showered benefits on their customers and users but also, for better or worse, developed the capacity to dominate the entire planet. As society now struggles to decide what to do about these new behemoths, it is useful to keep their possible flaws conceptually separate from the principles, processes and practices that enabled them to grow so fast. We need to keep in mind what good Agile looks like—essentially a better way for human beings to create more value other human beings. In any established organization, a small set of fairly stable principles (also known as mindset or management model) tends to guide decision-making throughout the organization.
Quote for the day: