First, banks have something most technologists don’t have enough of: Banks have domain expertise. Technologists tend to discount the exchange value of domain knowledge. And that’s a mistake. So much abstract technology, without critical discussion, deep product management alignment and crisp, clear and business-usefulness, makes too much technology abstract from the material value it seeks to create. Second, banks are not reluctant to buy because they don’t value enterprise artificial intelligence and other fintech. They’re reluctant because they value it too much. They know enterprise AI gives a competitive edge, so why should they get it from the same platform everyone else is attached to, drawing from the same data lake? Competitiveness, differentiation, alpha, risk transparency and operational productivity will be defined by how highly productive, high-performance cognitive tools are deployed at scale in the incredibly near future. The combination of NLP, ML, AI and cloud will accelerate competitive ideation in order of magnitude. The question is, how do you own the key elements of competitiveness? It’s a tough question for many enterprises to answer.
AI can drive value only if it is applied to a well-defined business problem, and you’ll only know if you’ve hit the mark if you precisely define what success looks like. Depending on the business objective, AI will commonly target profitability, customer experience, or efficiency. Automation from AI can yield cost savings or costs that are redirected to other uses. ... Treat your data as a treasured asset. While data quality and merging disparate data sources are common challenges, one of the biggest challenges in data integration initiatives is streamlining, if not automating, the process of turning data into actionable insights. ... If you are looking to develop AI capabilities in-house, keep in mind that AI teams can benefit from having a balance of skillsets. For example, deep expertise in modeling is critical for thorough research and solution development. Data engineering skills are essential in order to execute the solution. Your AI teams also need leaders who understand the technology, at least enough to know what is and is not possible. In running an AI team, it is important to create an environment that fosters creativity but provides structure. Keep the AI team connected to business leaders in the organization to ensure that AI is being applied to high-priority, high-value use cases that are properly framed.
Researchers have tried various ways to help computers predict what might happen next. Existing approaches train a machine-learning model frame by frame to spot patterns in sequences of actions. Show the AI a few frames of a train pulling out of a station and then ask it to generate the next few frames in the sequence, for example. AIs can do a good job of predicting a few frames into the future, but the accuracy falls off sharply after five or 10 frames, says Athanasios Vlontzos at Imperial College London. Because the AI uses preceding frames to generate the next one in the sequence, small mistakes made early on—a few glitchy pixels, say—get compounded into larger errors as the sequence progresses. Vlontzos and his colleagues wanted to try a different approach. Instead of getting an AI to learn to predict a specific sequence of future frames by watching millions of video clips, they allowed it to generate a whole range of frames that were roughly similar to the preceding ones and then pick those that were most likely to come next. The AI can make guesses about the future without having to learn anything about the progression of time, says Vlontzos.
There are clear opportunities to deliver this transformation through the adoption of a unified security approach. By this, we mean the integration, rationalisation and centralisation of security environments into a holistic ecosystem. Adopting such an approach can help improve the operator experience and make things simpler for the teams charged with maintenance – while also providing a cure to the headaches caused by platform proliferation. Not only this, but a unified security approach is a key enabler in helping security leaders engage at the board level by delivering cost transformation. An integrated security environment will serve to streamline operations for security teams, allowing staff to focus on higher value tasks while automating repetitive processes. In business terms, this means clawing back up to 155 days’ worth of effort for the average UK security team. Clearly, cost reduction and operational efficiencies are central to demonstrating business impact, but they should be viewed as a starting point rather than a security teams’ entire value proposition.
Neural network models also support multi-output regression and have the benefit of learning a continuous function that can model a more graceful relationship between changes in input and output. Multi-output regression can be supported directly by neural networks simply by specifying the number of target variables there are in the problem as the number of nodes in the output layer. For example, a task that has three output variables will require a neural network output layer with three nodes in the output layer, each with the linear (default) activation function. We can demonstrate this using the Keras deep learning library. We will define a multilayer perceptron (MLP) model for the multi-output regression task defined in the previous section. Each sample has 10 inputs and three outputs, therefore, the network requires an input layer that expects 10 inputs specified via the “input_dim” argument in the first hidden layer and three nodes in the output layer. We will use the popular ReLU activation function in the hidden layer. The hidden layer has 20 nodes, which were chosen after some trial and error. We will fit the model using mean absolute error (MAE) loss and the Adam version of stochastic gradient descent.
While its name is probably little-known to most viewers, Bunim Murray is kind of a big deal in TV. Founded in the late 1980s when two TV producers were flung together to produce a so-called ‘unscripted soap opera’ for the MTV network, the resulting show, The Real World, was instrumental in establishing the reality TV genre. The new company went on to develop global hits including Keeping Up With The Kardashians, Project Runway and The Simple Life. Bunim Murray’s CTO Gabe Cortina arrived at the firm with the infamous 2014 hack on Sony Pictures weighing on his mind. This incident centred on the release of The Interview, a comedy starring Seth Rogen and James Franco which depicted the fictionalised assassination of North Korean dictator Kim Jong-Un. Likely perpetrated by groups with links to the North Korean state, the large-scale leak of data from the studio caused great embarrassment for many high-profile individuals. From the get-go, Cortina understood that a similar kind of breach could be seriously damaging to Bunim Murray. “We’ve been in business for 30 years. We have a strong brand and we’re known for delivering high-quality shows,” he tells Computer Weekly.
To ensure a relatively secure browsing experience, browsers sandbox websites - providing only limited access to the rest of the computer and even other websites that are open on different tabs/windows. What differentiates Web Bluetooth/USB APIs to other Web APIs such as the MediaStream or Geolocation that received wide adaptation from all browser vendors is the specificity which they offer. When a user enters a website that uses the Geolocation API, the browser shows a pop-up requesting permission to access the current position. While approving this request can pose a security risk, the user makes a conscious decision to provide his or her location to the website. At the same time, the browser exposes a set of specific API calls (such as getCurrentPosition) that does exactly that. On the other hand, Bluetooth and USB communication work on a lower level, making it difficult to discern which actions are being performed by the website. For example, Web Bluetooth communicating with a device happens using the writeValue that accepts arbitrary data and can cause any number of actions on the target device.
What a regulated blockchain portends is that the impact the negative statements from government officials and the media along with regulatory uncertainties have been having on entrepreneurs, investors, the market, and the industry at large, will be a thing of the past. One area where we have started seeing the positive impact and transformation in technology is the case of the digital currency. The internet was the precursor of cashless policy and internet banking all of which greatly reduced the stress people had to go through to conduct businesses. The Chinese Government vehemently opposed cryptocurrency because it was decentralized but it’s of great relief to see that the People Bank of China (PBOC) is at the forefront of legitimizing digital currency. As a part of a pilot program, PBOC introduced a homegrown digital currency across four cities, this is a huge leap towards actualizing the first electronic payment system by a major central bank. The Bank of England (BoE) is also toeing the footsteps of China but at a review stage as of July 2020. Andrew Bailey, the Governor of BoE was reported to have said, “I think in a few years, we will be heading toward some sort of digital currency.”
This is a warning to CSOs and CISOs: Remove all sense of impropriety in IR. Concealing a data breach is illegal. Every decision made during an incident might be used in litigation and will be scrutinized by investigators. In this case, it's also led to criminal charges filed against a well-known security leader. If your actions seem to conceal rather than investigate and resolve a data breach, expect consequences. Neither the ransom nor the bug bounty are at issue here. Paying the ransom through the bug bounty was alleged to help conceal the breach. Firms should develop a digital extortion policy, so that there are no allegations of impropriety should they choose to pay a ransom. In addition, the guidelines of your bug bounty program should not be altered on the fly to facilitate non-bug bounty program activities. Work closely and openly with senior leadership on breaches and issues of ransom. Sullivan tried to get the hackers to sign non-disclosure agreements -- a legal document between two legitimate entities effectively acknowledging the hackers as business entities -- which allowed Uber to treat the hackers as third parties. Treating the ransom as a "cost of doing business" helped them conceal the payment from the management team as well.
Quote for the day:
"What I've really learned over time is that optimism is a very, very important part of leadership." -- Bob Iger