Big data, both in its structured and unstructured formats, have brought in unforeseen challenges in economics and business. How to organize, classify, and then analyze such data to obtain meaningful insights are the ever-going research topics for business leaders and academic researchers. This paper studies recent applications of deep neural networks in decision making in economical business and investment; especially in risk management, portfolio optimization, and algorithmic trading. Set aside limitation in data privacy and cross-market analysis, the article establishes that deep neural networks have performed remarkably in financial classification and prediction. Moreover, the study suggests that by compositing multiple neural networks, spanning different data type modalities, a more robust, efficient, and scalable financial prediction framework can be constructed.

Sufficiently capable models could subvert human oversight and decision-making in important contexts. For example, in the context of AI development, models could covertly sabotage efforts to evaluate their own dangerous capabilities, to monitor their behavior, or to make decisions about their deployment. We refer to this family of abilities as sabotage capabilities. We develop a set of related threat models and evaluations. These evaluations are designed to provide evidence that a given model, operating under a given set of mitigations, could not successfully sabotage a frontier model developer or other large organization's activities in any of these ways. We demonstrate these evaluations on Anthropic's Claude 3 Opus and Claude 3.5 Sonnet models. Our results suggest that for these models, minimal mitigations are currently sufficient to address sabotage risks, but that more realistic evaluations and stronger mitigations seem likely to be necessary soon as capabilities improve. We also survey related evaluations we tried and abandoned. Finally, we discuss the advantages of mitigation-aware capability evaluations, and of simulating large-scale deployments using small-scale statistics.

Before we look into benefits AI offers to business, let's look at what AI really is. Despite the rumours spread mainly by sci-fi films, AI cannot completely replace humans, but can make our lives and the ways we do business much easier, allowing us to find better solutions and supporting us in our discoveries. Currently, the global AI market is valued at over $136 billion and is expected to increase by over 13x in the next eight years, reaching an incredible number of $1.81 trillion by 2030. That alone speaks of the importance of Artificial Intelligence and its incredible potential. The statistics are impressive, but how do businesses actually use Artificial Intelligence?

Velocity has integrated this latest advancement in artificial intelligence with its existing analytics tool – Velocity Insights. Velocity Insights is India's largest eCommerce analytics platform that is trusted by 3000 Indian eCommerce brands to make informed business decisions. Brands that utilize Insights receive a daily business report on Whatsapp. Further, these brands can also access benchmarks to evaluate their performance relative to other brands in the industry. Velocity Insights currently provides advanced analytics that contains information about an eCommerce business's sales and marketing performance.

Mex is a marketing technology leader, architect and strategist who talks about martech and AI. Decision intelligence (DI) is how people make business decisions, regardless of their role or industry. The key factor in decision intelligence is its focus on outcomes, emphasizing results, which gives both commercial decision makers and technical teams a space to address actual business issues. What decision intelligence is not is removing humans from the decision-making process completely. It's about empowering humans with AI and creating a more holistic, convenient view of all of your business' data to allow them to make the best decision possible.

Our world has become increasingly digital, and business leaders need to make sense of the enormous amount of available data today. In order to make key strategic business decisions and leverage data as a competitive advantage, it is critical to understand how to draw key insights from this data. The Business Analytics specialization is targeted towards aspiring managers, senior managers, and business executives who wish to have a well-rounded knowledge of business analytics that integrates the areas of data science, analytics and business decision making. The courses in this Specialization will focus on strategy, methods, tools, and applications that are widely used in business. Topics covered include: Data strategy at firms Reliable ways to collect, analyze, and visualize data–and utilize data in organizational decision making Understanding data modeling and predictive analytics at a high-level Learning basic methods of business analytics by working with data sets and tools such as Power BI, Alteryx, and RStudio Learning to make informed business decisions via analytics across key functional areas in business such as finance, marketing, retail & supply chain management, and social media to enhance profitability and competitiveness.

Artificial Intelligence is a combination of several technologies that allow machines to sense, learn, understand, and augment human activities. This technology was presented for the first time in 1956 at Dartmouth. Since then, there has been so much optimism about AI that machines were expected to perform human functions within 20 years. This technology was forced to enter an "AI winter" phase when funds ran out. Things have changed dramatically since then.

Lapses such as racially biased facial recognition or apparently sexist credit card approval algorithms have thankfully left companies asking how to build AI ethically. Many companies have released "ethical AI" guidelines, such as Microsoft's Responsible AI principles, which requires that AI systems be fair, inclusive, reliable and safe, transparent, respect privacy and security, and be accountable. These are laudable, and will help prevent the harms listed above. Harm can result from what a system is used for, not from unfairness, black-boxyness, or other implementation details. Consider an autonomous Uber: if they are able to recognize people using wheelchairs less accurately than people walking, this can be fixed by using training data reflective of the many ways people traverse a city to build a more fair system.

Hello AI&ML Engineers, as you all know, Artificial Intelligence (AI) and Machine Learning Engineering are the fastest growing filed, and almost all industries are adopting them to enhance and expedite their business decisions and needs; for the same, they are working on various aspects and preparing the data for the AIML platform with the help of SMEs and AIML Experts to build the solutions. Things are not stopping there. To give more clarity, end users or stakeholders are looking for more clarity on solutions and justifications. This grey area is the so-called Black-Box. Now in industry, the expensive addon in this series is the so-called Explainable AI (XAI) and hope you heard about this terminology.

Gartner has anointed "Hyperautomation" one of the top 10 trends for 2022. Is it a real trend, or just a collection of buzzwords? As a trend, it's not performing well on Google; it shows little long-term growth, if any, and gets nowhere near as many searches as terms like "Observability" and "Generative Adversarial Networks." And it's never bubbled up far enough into our consciousness to make it into our monthly Trends piece. However, that skeptical conclusion is too simplistic. Hyperautomation may just be another ploy in the game of buzzword bingo, but we need to look behind the game to discover what's important. There seems to be broad agreement that hyperautomation is the combination of Robotic Process Automation with AI. Natural language generation and natural language understanding are frequently mentioned, too, but they're subsumed under AI. So is optical character recognition (OCR)–something that's old hat now, but is one of the first successful applications of AI. Using AI to discover tasks that can be automated also comes up frequently. While we don't find the multiplication of buzzwords endearing, it's hard to argue that adding AI to anything is uninteresting–and specifically adding AI to automation. Get a free trial today and find answers on the fly, or master something new and useful.