The researchers suggested that any algorithm that sought to ensure a superintelligent AI cannot harm people had to first simulate the machine’s behavior to predict the potential consequences of its actions. This containment algorithm then would need to halt the supersmart machine if it might indeed do harm. However, the scientists said it was impossible for any containment algorithm to simulate the AI’s behavior and predict with absolute certainty whether its actions might lead to harm. The algorithm could fail to correctly simulate the AI’s behavior or accurately predict the consequences of the AI’s actions and not recognize such failures. “Asimov’s first law of robotics has been proved to be incomputable,” Alfonseca says, “and therefore unfeasible.” We may not even know if we have created a superintelligent machine, the researchers say. This is a consequence of Rice’s theorem, which essentially states that one cannot in general figure anything out about what a computer program might output just by looking at the program, Alfonseca explains. On the other hand, there’s no need to spruce up the guest room for our future robot overlords quite yet. Three important caveats to the research still leave plenty of uncertainty to the group’s predictions.
A change made within on-premises Active Directory by an attacker can provide access to much more than just local resources. An attacker, can for example, make a compromised on-premises user account a member of a Sales group in Active Directory. This group likely would provide access to on-premises systems, applications, and critical data. But because Active Directory often federates with cloud applications via external IDP (e.g., Azure AD), it’s reasonable to assume that this same change in membership could allow access to a cloud-based CRM environment (like Salesforce), customer data (hopefully contained to the breached account, but more likely to the entire organizational data) and other resources. In many cyberattacks it’s more complex than the example above, where it’s necessary to gain elevated privileges via one account only to compromise a second, third, and so on, each time moving from system to system, or – in the case of a hybrid environment – from on-premises to cloud, leveraging access to on-premises Active Directory to specifically target accounts known to have access in the cloud.
Building AI in-house presents a variety of benefits. When done right, a built approach can lead to a stable, production-grade AI solution that is perfectly tailored to the specific needs and requirements of an industry or company. Digital natives have shown the impact of building AI from scratch. IBM is a prominent example of a business that has launched successful in-house AI into production. A recent report found IBM’s Watson Assistant AI paid itself back in just 6 months, with a three-year ROI of 337%. For digital adopters however, successfully building and implementing an AI solution in house is easier said than done without access to sizable capital and infrastructure. “When building an AI solution in-house, companies typically hire a team without significantly investing in the foundational elements that are required to stabilize AI in complex and dynamic environments,” suggests Nurit Cohen Inger, VP of Products at AI company BeyondMinds. “This approach, unfortunately, has typically meant a long and costly process to reach ROI positivity or in the worst case, never achieving production. Before developing AI solutions, businesses must heavily invest in solving the barriers that hold them back from turning proof of concepts into successful solutions in production.”
It’s very tempting to put everything you know on a training agenda, especially when you, as a trainer, feel that you have to know everything and constantly impress the learners. It’s always hard to chop workshop content into the bare minimum, especially when you have a lot of knowledge, experience, and fun stories to share. But if you are aiming for deep understanding and a lot of practice, less content translates into more value. Overloading groups with new information may lead to chaos during your class. They will struggle to understand which new tool or technique they should use first. In the end, they may just quit before they even start. ... Training From the Back of the Room (TBR) is a fresh approach to learning, training, presenting and facilitating that was developed by Sharon Bowman. It uses cognitive neuroscience and brain-based learning techniques to help learners to retain new information. TBR teaches you how to engage the five senses and keeps your learners active and engaged throughout the class. The concept is recognized internationally as one of the most effective frameworks for accelerated learning. It is a new way of teaching adults.
The pandemic reminded us that we’re human. This experience has taught us compassion, grace, and the importance of both the health and wellbeing of ourselves and our families. COVID-19 has fundamentally reshaped the way we view protection products. In fact, two thirds (66%) of Americans say they now better understand life insurance’s value, with another quarter buying coverage for the first time. Awareness around the role of employers in providing access to these products has also increased. In a recent LIMRA study, one in four employees said they are more likely to sign up for certain benefits available through their employer. Along with this heightened awareness of our mortality and morbidity comes the realization that we thrive on human interaction. We can’t take a digital-only approach. Bringing emotion—positive emotion and empathy—to the experience and every interaction we have with customers will help us get farther, faster. As we continue to invest in technology across the insurance industry, we need to look for ways to make digital and human experiences work together for customers, employers, and financial professionals. Many of our customers tell us they don’t understand insurance products and they don’t know where to start educating themselves.
To explain the way that the “experience gap” might cause trouble, I'd like to share a real-life example. Several years ago a quite known and respectable Central European bank embarked on a voluminous digital transformation journey. The bank's application had a rating of 3.5 and was outdated. In order to digitalize, improve the bank's image and the competitive chances in the growing digital market, the management intended to urgently create and launch a modern looking banking application. Therefore, the initial design and development period was 6 months. Nevertheless, the bank spent three times as much time building the new application by themselves: 1 year and 8 months. This was a serious project not only in terms of time but also the budget invested. Judging by the scope of the project, the improvements made and the timeline, the overall costs could be estimated at around half a million. However, the result did not live up to expectations at all. After the new application was released it decreased to 2.4 from the previous 3.5 and has kept dropping even a year after its first release as it did not improve, but significantly worsened the customer experience.
Disruption is not necessarily the crisis it’s frequently considered to be for incumbents, the researchers stress. Two technologies can often coexist in the marketplace for a significant period. Thus, it’s important for incumbent companies not to overreact. They should target dual users and reexamine the factors that have led to the old technology sticking around for so long. Of course, the profit implications of cannibalization of the old technology and leapfrogging depend on which type of firm is trumpeting the new technology. New entrants will always stand to gain when they introduce a technology that takes off. But incumbents rolling out a successive technology will also gain if their competitors would have introduced it anyway or if the 2.0 version has a higher profit margin than the original. The authors write, “Leapfroggers are an opportunity loss for incumbents, but switchers are a real loss.” Regardless of the predictive model they use, marketers should strive to understand how the various consumer segments identified in this study will grow or shrink over time and use that information in their forecasts of early sales or market penetration of successive technologies.
What’s worse is that machine learning models can’t tell right from wrong and make moral decisions. Whatever problem exists in a machine learning model’s training data will be reflected in the model’s behavior, often in nuanced and inconspicuous ways. For instance, in 2018, Amazon shut down a machine learning tool used in making hiring decisions because its decisions were biased against women. Obviously, none of the AI’s creators wanted the model to select candidates based on their gender. In this case, the model, which was trained on the company’s historical hiring data, reflected problems within Amazon itself. This is just one of the several cases where a machine learning model has picked up biases that existed in its training data and amplified them in its own unique ways. It is also a warning against trusting machine learning models that are trained on data we blindly collect from our own past behavior. “Modeling the world as it is is one thing. But as soon as you begin using that model, you are changing the world, in ways large and small. There is a broad assumption underlying many machine-learning models that the model itself will not change the reality it’s modeling. In almost all cases, this is false,” Christian writes.
First, there needs to be a government-wide, comprehensive digital skills strategy. One survey of industry professionals found that 40% of public sector organisations did not have the right skills to carry out digital transformation. Every member of the workforce needs to be able to perform basic tasks online. But to press forward with digital transformation, the government needs to champion digital leadership in the public sector – and that includes paying properly for those skills. The Government Digital Service recently advertised for a head of technology and architecture with a maximum salary of £70,887 a year. According to Google Jobs, typical pay for this type of work ranges from £65,000 to £180,000 in the private sector. This puts the public sector at a unique disadvantage and pay scales should be reviewed. ... Second, the Cabinet Office needs to address the gap between guidance and action on the ground. Out-of-date technology is widespread in some areas of the public sector, despite there being a large volume of information from central government on maintaining and updating digital infrastructure. Legacy IT has been holding digital public services back for years and will continue to do so unless there is a cross-government push to drive this forward.
Emotion detection would be a lot easier if humans expressed themselves in homogenous ways. However, cultural backgrounds and unique life experiences influence personal expression. Michelle Niedziela, VP of research and innovation at market research firm HCD Research, said advertisers and their agencies can get overly excited about the "happy" responses an ad drives when the response may have been a natural reflex. "If I smile at you, you innately smile back. So, one thing is are they really feeling happy or just projecting happy?" said Niedziela. "But also, how big does a smile have to be in order to be interpreted as happy?" Even cheap camera sensors are improving, but some of them may not be able to detect subtle nuances in facial geometry or provide the same degree of reliability among individuals who represent different races. Also, things that change an individual's appearance like hats, bangs or facial hair can negatively impact the accuracy of emotion sensing. "In my mind, the two biggest challenges are hardware quality and the models," said Capgemini's Simion. "You need to be very careful when you're talking about emotionality is the dataset you're going to use because if you're just going to call normal APIs from the cloud providers, that's not going to help much."
Quote for the day:
"To do great things is difficult; but to command great things is more difficult." -- Friedrich Nietzsche