The Aite Group in its report "Hedge Fund Survey, 2020: Algorithmic Trading" argues that the main reason for the growing popularity of algorithms in trading is to try to reduce the influence of the human factor on the market due to its high volatility. The economic fallout from COVID-19 has seen a record-breaking drop in the American, European, and Chinese stock markets. And only a few months later, measures to stimulate the economy were able to stop the fall and reverse the downtrend up. Thus, we get the first task of Algorithmic Trading - risk reduction in a market with high volatility. The second global advantage of algorithmic trading lies in the ability to analyze the potential impact of trade on the market. This can be especially useful for Hedge Funds and institutional investors who handle large sums of money with a visible effect on price movements. The third fundamental advantage of trading algorithms is protection from emotions. Traders and investors, like all living people, experience the emotions of fear, greed, lost profits, and others. These emotions have a negative impact on performance and results.
Employees are the weakest link in any organization’s security posture. A Tessian report found that 43% of US and UK employees have made mistakes that resulted in cybersecurity repercussions for their organizations. Phishing scams, including emails that try to trick employees into sharing corporate login details, are particularly common. Educating employees on cyber threats and how to spot them is crucial to mitigating attacks. However, for training to be effective, it needs to consist of more than just repetitive lectures. In the report mentioned above, 43% of respondents said a legitimate-looking email was the reason they fell for a phishing scam, while 41% of employees said they were fooled because the email looked like it came from higher up. Live-fire security drills can help employees familiarize themselves with real-world phishing attacks and other password hacks. Safety awareness training should also teach workers the importance of good practices like using a virtual private network (VPN) when working from home and making social media accounts private.
Technology leaders should regularly use their own technology to better identify pain points and opportunities for improvements. That means that I should be teaching and using the same systems that faculty does to understand their experience through their lens. I should be meeting regularly with them and generating a Letterman-style Top 10 list of the things I hate most about my technology experience. This is something to do with the students, too. What do they hate most about the technology at the university? And how can we partner with them to address these issues over the next 12 months? Several years ago, for example, we reexamined our application process. If a prospective student wanted to submit an application, we required them to generate a unique username and password. If the one they chose was already taken, they needed to continue creating alternate versions until they eventually landed upon one that was available. If someone began the application process and logged off to complete it later, then forgot their username and password, they’d have to start all over again. It was absurd.
AI is increasingly converging the traditional high-performance computing and high-performance data analytics pipelines, resulting in multi-workload convergence. Data analytics, training and inference are now being run on the same accelerated computing platform. Increasingly, the accelerated compute layer isn’t limited to GPUs—it now involves FPGAs, graph processors and specialized accelerators. Use cases are moving from computer vision to multi-modal and conversational AI, and recommendation engines are using deep learning while low-latency inference is used for personalization on LinkedIn, translation on Google and video on YouTube. Convolutional neural networks (CNN) are being used for annotation and labeling to transfer learning. And learning is moving to federated learning and active learning, while deep neural networks (DNN) are becoming even more complex with billions of hyper-parameters. The result of these transitions is different stages within the AI data pipelines, each with distinct storage and I/O requirements.
Gartner's analysts say that "work from anywhere" and cloud-based computing have accelerated cloud-delivered SASE offerings to enable anywhere, anytime secure access from any device. Security and risk management leaders should build a migration plan from the legacy perimeter and hardware-based offerings to a SASE model. One hindrance to SASE adoption, some security experts tell me, is that organizations lack visibility into sensitive data and awareness of threats. Too many enterprises have separate security and networking teams that don't share information and lack an all-encompassing security strategy, they say. "While the vendors are touting SASE as the end-all solution, the key to success would depend upon how well we define the SASE operating model, particularly when there are so many vendors coming up with SASE-based solutions," says Bengaluru-based Sridhar Sidhu, senior vice president and head of the information security services group at Wells Fargo. Yask Sharma, CISO of Indian Oil Corp., says that as data centers move to the cloud, companies need to use SASE to enhance security while controlling costs.
If you were designing the managerial structure for a software development firm from scratch today, it’s very unlikely that you would separate NetOps and SecOps in the first place. Seen from the perspective of 2021, many of the monitoring and visibility tools that both teams seek and use seem inherently similar. Unfortunately, however, the historical development of many firms has not been that simple. Teams and remits are not designed from the ground up with rationality in mind – instead they emerge from a complex series of interactions and ever-changing priorities. This means that different teams often end up with their own priorities, and can come to believe that they are more important than those of other parts of your organization. This is seen very clearly in the distinction between SecOps and NetOps teams in many firms. At the executive level, your network exists in order to facilitate connections – between systems and applications but above all between staff members. Yet for many NetOps teams, the network can come to be seen as an end in itself.
“Enterprise data is growing nearly exponentially, and it is also increasing in complexity in terms of data types,” said Morgan. “We have gone way past the time when humans could sift through this amount of data in order to see large-scale trends and derive actionable insights. The platforms and best practices of data science and data analytics incorporate technologies which automate the analytics workflows to a large extent, making dataset size and complexity much easier to tackle with far less effort than in years past. “The second value-add is to leverage machine learning, and ultimately artificial intelligence, to go beyond historical and near-real-time trend analysis and ‘look into the future’, so to speak. Predictive analysis can unveil new customer needs for products and services and then forecast consumer reactions to resultant offers. Equally, predictive analytics can help uncover latent anomalies that lead to much better predictions about fraud detection and potentially risky behaviour. “Nothing can foretell the future with 100% certainty, but the ability of modern data science to provide scary-smart predictive analysis goes well beyond what an army of humans could do manually.”
DevOps has transformed itself in the last few years, completely changing from what we used to see as siloed tools connected together to highly integrated, single-pane-of-glass platforms. Collaboration systems like JIRA, Slack, and Microsoft Teams are connected to your observability tools such as Datadog, Dynatrace, Splunk, and Elastic. Finally, IT Service management tools like PagerDuty are also connected in. Tying these high-in-class tools together on one platform, such as the JFrog Platform, yields high value to the enterprises looking for observability workflow. The security folks also need better visibility into an enterprise’s systems, to look for vulnerabilities. A lot of this information is available in Artifactory and Amazon Web Services‘ Xray, but how do we leverage this information in other partner systems like JIRA and Datadog? It all starts with JFrog Xray’s security impact, where we can generate the alert to Slack and robust security logs to Datadog to be analyzed by your Site Reliability Engineer. A PagerDuty incident that’s also generated from Xray can then be used to create a JIRA issue quickly.
The line between digital and physical has blurred, with consumers who once preferred brick-and-mortar engagements now researching, shopping and buying using digital channels more than ever. This trend is expected to increase across all industries. While organizations have enabled improved digital engagement over the past several months, there are still major pain points, mostly with speed, simplicity and cross-channel integration during the ‘first mile’ of establishing a relationship. The retail industry already understands that consumers are becoming increasingly impatient, wanting the convenience and transparency of eCommerce and the service and humanization of physical stores. In banking, consumers are diversifying their financial relationships, moving to fintech and big tech providers that can open relationships in an instant and personalize experiences. According to Brett King, founder of Moven and author of the upcoming book, ‘The Rise of Technosocialism’, “The ability to acquire new customers at ‘digital scale’ will impact market share and challenge existing budgets for branches. ..."
Contextual AI can be divided into three pillars that help make businesses become more visible to the people they want to reach. In the same sense, when a business is looking for a partner, it has to be sure that a prospect can offer the right services to fulfill its goals. Contextual AI aims to deliver that. The technology allows a brand to enhance its understanding of consumer interests. It is easy to make assumptions about consumer interests in different sectors, but difficult to prove them. ... In previous years, contextual AI was seen as an enhancing technology, but not an essential one. Now, the recognition of contextual AI as more than simply enhancing is growing. Businesses are constantly looking for more cost-effective solutions to their problems, and contextual AI offers one solution to fit that bracket. If you look at a similar alternative, such as behavioral advertising, it is heavily reliant on data — and lots of it. The huge amounts of data required to make this a success means that businesses have to implement a successful collection, analysis and then reporting solution in order to leverage it effectively. This can be a costly process if a business does not have large economies of scale.
Quote for the day:
"A true dreamer is one who knows how to navigate in the dark" -- John Paul Warren