Looking at the current cloud market trends is like a quick glimpse into the future. Whatever innovative technology is on the menu, you can be sure that the big players are investing. Discover more in Henstu’s Weekly Cloud Trends.

The State of Machine Learning in 2020

To overcome the hardships of 2020, quite a lot of companies are looking for pioneering technologies as the means to carry out. Certain businesses are not only managing to endure that way but are also flourishing. Apart from cloud computing, machine learning and AI are on the radar as well. According to the latest reports, firms are investing in the benefits of deep learning and ML. Here’s a snippet: “Along with questions about AI adoption and implementation, the McKinsey State of AI report examines companies whose AI applications led to EBIT growth of 20% or more in 2019. Among the report’s findings: Respondents from those companies were more likely to rate C-suite executives as very effective, and the companies were more likely to employ data scientists than other businesses were.” Moreover, "At rates of difference of 20% to 30% or more compared to others, high-performing companies were also more likely to have a strategic vision and AI initiative road map, use frameworks for AI model deployment, or use synthetic data when they encountered an insufficient amount of real-world data. These results seem consistent with a Microsoft-funded Altimeter Group survey conducted in early 2019 that found half of high-growth businesses planned to implement AI in the year ahead." Check out the full story over at VentureBeat.

Achieving 100% Renewable Energy with 24/7 Monitoring in Microsoft Sweden

Microsoft is known for making a commitment to creating 100% renewable energy supply for their buildings and datacenters by the year 2025. Remember, back in May 2020, we have seen how other major cloud providers are competing to have the “greenest way to store data.” Here's the buzz on the company’s latest effort in that department: "Today, we are announcing that Microsoft will be the first hyper-scale cloud provider to track hourly energy consumption and renewable energy matching in a commercial product using the Vattenfall 24/7 Matching solution for our new datacenter regions in Sweden, which will be available in 2021." Find out more at Microsoft's blog.

Salesforce in Talks to Buy Slack

Cloud-based software company Salesforce has unveiled its intentions to purchase Slack, the well-known business communication platform. You might think Salesforce is not too big a player in the cloud game, but nothing can be farther from the truth. Fortune reports that "Microsoft is the playing the long game too. In addition to battling Slack with Microsoft Teams, it has got Dynamics, its own Salesforce-fighting customer-relationship management software. But as the number two cloud-computing business behind only Amazon, Microsoft has less to worry about than Google, which may be provoked by Salesforce’s swaggering to make a bold bet on collaboration software of its own in the coming months." Continue reading more about this scoop, at Forbes.

Date/Time

Date(s) - 01/01/1970
12:00 AM - 12:00 AM

Location

600 5th ave. NY, NY
Every bit of the technology scene is seeing progress, and for the past few months we’ve seen quite a wide range of innovative solutions coming to the cloud. More than that, we are also witnessing the birth of quantum computing, which they say is going to be the next stage of cloud computing. We’ve also learned about some significant discoveries in the domain of machine learning and AI. For example, in one of our recent weekly news round-ups, we’ve discovered how artificial intelligence can now learn unsupervised at the speed of light. Dig into the latest scoops in the Hentsu weekly tech news round-up.

Universal Filmed Entertainment Group and MS Azure Announce Partnership to Accelerate Live-action and Animation Productions

In the past year or so Microsoft added some rather cool and unique features to its cloud-based services. Lately, MS Teams launched diverse additions that are great for meetings and collaboration in general. Today, we’ve learned that Universal Filmed Entertainment Group and Microsoft a officially entered a strategic partnership to “cloud-optimize live-action and animation productions.” The goal here is to give creative communities to opportunity to utilize cloud-based production workflows, thus unlocking improved remote collaboration and content creation. In addition, teams can now extend DreamWorks Animation’s cutting-edge production platform to include live-action production. Those workflows are then brought into Microsoft Azure. “Together with customers like Universal and DreamWorks, we are prioritizing cloud + edge technologies to help transform workflows, increase production output and reduce friction for creatives,” said Hanno Basse, media and entertainment CTO, Microsoft Azure. “Working together, we aspire to create technology for the industry, with the industry, so they can tell stories the world loves.” Read the full scoop at Microsoft News.

Foiling Fraud with Machine Learning

For the past year or so there has been a lot of developments in tech, especially in terms of digital transformation. While some regarded as nothing more than a buzzword, the latest achievements in tech have indicated otherwise. However, as more and more people are going digital, this makes them more open to fraud. Word is “In a highly dynamic environment where fraudsters are discovering new attack vectors every day, it’s critical for fraud prevention teams to be able to detect threats and respond quickly. Artificial intelligence and machine learning (AI/ML) approaches can help by spotting patterns in previous fraud cases and using them to detect suspicious behavior by customers, employees or systems.” Check out the full story at TechRadar.

IBM Hits New Quantum Computing Milestone

Recently, an array of developments have occurred in the world of quantum computing, with some cool solutions already appearing on the market. For instance, AWS launched Amazon Bracket, which offers a development environment allowing customers to explore and design quantum algorithms. Now, IBM has revealed they have reached a new quantum computing milestone, @hitting its highest Quantum Volume to date. Using a 27-qubit client-deployed system, IBM achieved a Quantum Volume of 64.” "IBM's full-stack approach gives a unique avenue to develop hardware-aware applications, algorithms and circuits, all running on the most extensive and powerful quantum hardware fleet in the industry," Jay Gambetta, IBM Fellow and VP of IBM Quantum, said in a statement. Read the rest of the story at ZDnet.

Date/Time

Date(s) - 01/01/1970
12:00 AM - 12:00 AM

Location

600 5th ave. NY, NY
Portfolio choice is a non-trivial problem faced by economic agents. In a nutshell, it involves making a decision regarding how one should distribute one's wealth across multiple assets. Distinct assets offer their unique outcome possibilities. The two dimensions to be taken into account in the process of decision making are "expected return" and "risk". The former characterizes the percentage of increase or decrease in a given investment i.e. the income generated by it over a time period such as a month, a quarter, or a year. The latter quantifies the uncertainty of the payoffs to the investor. Risk factors span beyond the marketplace, including political and economic events, and in some cases, even weather has an impact on many commodities.

True Value of AI and ML

In order to evaluate portfolio risk, the scientist called James Kelly came up with a mathematical formula, naturally named the Kelly Criterion. Skipping on the technical details, the principle boils down to the following: the more comprehensive and of higher quality our information is, the lower our risk becomes. You have probably heard about machine learning and potentially even wondered whether it could be utilized to benefit your business in addition to everyday life. The great news is that the applications of this subset of Artificial Intelligence are not restricted to self-driving cars and automatic tagging of photos. This field can be brought into practice to portfolio choice in a twofold manner. Firstly, using and tackling the ever-growing information from historical market data and financial valuations, we can model it to make predictions or forecasts. In the machine learning context, this means we use the available past data for the training process. If we apply deep learning algorithm, the model will learn how the different inputs, or feature, such as revenue growth rate and terminal growth rate, can influence a firm's final value. It is worth noting the current power of supercomputers: tasks that would have taken weeks to solve several years ago now take up to several hours, so with the accelerating speed of technology come faster results. Secondly, we can use machine learning to avoid the common hazard of overfitting. The issue with overfitting often stems from the temptation for analysts to believe they have unraveled a relationship in the data after tweaking parameters or mistakenly assume spurious correlations as meaning causation. Machine learning enables us to bypass such problems by restricting human involvement to set up the whole framework for investing. The system can then explore and discover an optimal investment strategy and perform direct allocation choices.

Machine Learning Portfolio Management and the Cloud

Portfolio management within the cloud space allows businesses to govern existing services, new services, and how they set up relationships with cloud providers. As cloud adoption and cloud computing get more popular, management services are what bridges the gap between companies and digital transformation. If you are looking to leverage powerful and modern tech offered by public service providers like Amazon, Google, or Microsoft, machine learning portfolio management is but the tip of the iceberg in terms of what's possible. We believe that methods from artificial intelligence will become increasingly important in the field of investment management over the next years. The process of being able to spot an opportunity in the market and come up with an investment approach is both technical and creative. Through the use of machine learning algorithms and automating certain aspects of the strategies' creation, asset managers will enhance their accuracy, efficiency, and potentially boost returns.  If you wish to read more on the topic of AI and deep learning, have a look at our recent blog post: Machine Learning: Practical Application in Trading. In the meantime, reach out to us if you need help to learn what cloud technology or ML aligns best with your business: hello@hentsu.com

Date/Time

Date(s) - 01/01/1970
12:00 AM - 12:00 AM

Location

600 5th ave. NY, NY
Selecting factors is important in Quantitative Finance. Due to the explosion of information, it is becoming harder to select and monitor these factors without additional help such as using automatic algorithms. In machine learning, this topic is studied in the area known as factor selection. Factor selection in finance can be a unique and specialised challenge, without an off-the-shelf solution. Instead, one should consider some adaptive approaches to factor selection in a well-controlled manner. Two key properties that we want to evaluate are predictability and diversity.

Predictability

Predictability means that chosen factors will have predictive power for a target variable. For example, if the target variable is the S&P 500 index, there should be a collection of factors which are potentially related to the index. These include factors such as technical indicators or macroeconomic variables. The factor selection algorithm is usually backed by supervised models, such as lasso regression, or a gradient boosting machine. These algorithms can produce a measure of importance of input data, which serve for selecting the most useful factors from a larger set.

Diversity

However, blindly using the factor selection algorithms may lead to a pointless answer, such as the selected factors providing redundant information. To alleviate this issue, we introduce the second metric: diversity. Diversity means the factors selected from this system should have low correlation. To enforce diversity, one could cluster the variables into highly correlated groups using some dimensional reduction algorithms. In this process, we care about the correlation within the groups and lack of correlation across groups. Each group then becomes a new derived variable on which we can further run the factor selection algorithm.

Factor Elimination

A benefit of the system is that instead of generating the result in one round of execution, we can run it several times in an iterative fashion. This allows us to gradually refine the process of eliminating factors. Using cloud compute, we can easily and efficiently go through huge data sets and perform this analysis. For example, we target predicting the S&P 500 index for the next trade day by around 1000 factors. It is clearly unwise to use all 1000 factors, and we would want a more concise model which can be included in the final model. If we run the system for 5 rounds, and it roughly halves the number of remaining factors for each round. Finally, it ends up with about 30 factors which can now be manually inspected to ensure they are sensible. For more information stay tuned for our next Cognitive Cloud event focused around AI and Machine Learning in Trading.

Date/Time

Date(s) - 01/01/1970
12:00 AM - 12:00 AM

Location

600 5th ave. NY, NY