Companies have gotten the message that artificial intelligence should be implemented in a manner that is fair and ethical. In fact, a recent study from Deloitte indicates that a majority of companies have actually slowed down their AI implementations to make sure these requirements are met. But the next step is the most difficult one: actually implementing AI in a fair and ethical way. A Deloitte study from late 2019 and early 2020 found that 95% of executives surveyed said they were concerned about ethical risk in AI adoption. While machine learning brings the possibility to improve the quantity and quality of decision-making based on data, it also brings the potential for companies to damage their brand and reduce the trust that customers have placed in it if AI is implemented poorly.

A variety of financial services companies have started to incorporate artificial intelligence (AI) into their operations-- ranging from quantitative asset managers that use machine learning (ML) models to predict price movements in securities to roboadvisor systems that use AI to help investors decide on their asset allocation. More broadly, companies are increasingly using AI to both analyze structured data, (e.g., asset flows, performance) and extract information from unstructured/alternative data (e.g., images, documents, social media posts) through image recognition and natural language understanding capabilities. The greater volume of data, along with AI and ML tools that can provide automated insights and analytics, offers significant opportunities for asset owners and asset managers to increase operational productivity, improve cybersecurity and manage risk, among other benefits. Currently, more than half of asset managers are in the early stages of AI initiatives, according to a Sapient Global Markets survey.1 And almost one-quarter of asset owners who invest in hedge funds use alternative data and big data analytics/AI to support their investment processes, according to an EY/Greenwich Associates survey.2

CAPTURING data in today's world is easy. Be it an action in the digital world on a website or an application or in the physical world in a retail or commercial environment -- everything can be tracked. Making sense of that data, however, requires more than just employing data scientists. Founder and Chief of Product Angad Chowdhry told Tech Wire Asia that subject matter expertise is the most important piece of the puzzle when it comes to making sense of data. Chowdhry's company works with a variety of for-profit and non-for-profit businesses, tapping into data from a plethora of sources, and running artificial intelligence (AI)-powered models to answer questions that help better understand markets, invest resources, and plan for the future. Quilt.ai recently collaborated with School of Oriental and African Studies (SOAS) and the Barbican Centre in the UK on a project to help AI understand the context when it sees a photograph.

Digital innovation requires enterprises to learn how to understand, manage and change increasingly complicated processes. A new generation of process mining tools promises to make it easier to automatically interpret the digital exhaust of modern enterprises to help improve decision-making, drive innovation, and offer new products and services. "By understanding how processes really operate, companies can create operational fluidity to drive more efficient and productive operations that create better customer experiences," said Alexander Rinke, CEO and co-founder of Celonis, a process mining platform based in Germany. "Instead of simply identifying areas of friction, AI will further evolve process mining by allowing businesses to implement recommended changes with employees, enhancing productivity while also saving resources." The core idea of process mining lies in finding new ways to create and calibrate models of how things work with event logs.

To have real value in healthcare, machine learning must be actionable. Too often, IT decision makers don't take a step back and ask if ML makes for a good business model, said Vikas Chowdhry, chief analytics and information officer at Parkland Center for Clinical Innovation. Sometimes, big ideas are better left off the table. "Oftentimes, people forget this," said Chowdhry, speaking this past week during the HIMSS Machine Learning and AI for Healthcare conference in Boston. "We think every problem can be solved with machine learning.

Recently, analyst Trip Chowdhry of Global Equities Research wrote in an investor note that Wal-Mart Stores (NYSE:WMT) will ramp up its focus on deep neural networks for its OneOps cloud business and that the retailer will tap NVIDIA's (NASDAQ:NVDA) graphics processing units (GPUs) to make this happen. Deep neural networks are used in artificial intelligence processing to allow computers to understand the relationships between pieces of information without having to be specifically programmed to understand that the information is related. Deep neural networks, and the broader deep learning segment, are part of a growing artificial intelligence market. Chowdhry thinks the ramp-up of Wal-Mart's cloud will happen over the next six months and will be "incrementally positive" to NVIDIA's GPU business. These rumors come after reports surfaced in June that Walmart was asking some of its technology customers to move off of Amazon's Web Service (AWS) cloud business.