How AI and ML are shaking up the asset management industry
Crises, when they occur, have one very clear advantage: they force us to think and accelerate new processes. In 2020, we have so far seen the COVID-19 crisis cause widespread concern and economic hardship for consumers, businesses and communities across the globe. The asset management industry has also taken a step back to re-evaluate the way that it functions.
Advanced technology has begun to change as AI and machine learning takes an ever-greater portion of the asset management industry. It is invading further and further into the wider strategy domain, which was once the exclusive territory of human analysts.
Like any industry, asset management practitioners must adapt if they are to survive. Whether you are at home or in the workplace, technology such as AI and machine learning has become more central to our lives this year than ever before. From allowing our children to sit exams at home, to contributing to the quest for a COVID vaccine; high-performing companies and organisations have increased their investment in AI amid the COVID-19 crisis, and this trend will only continue to rise.
Although often used interchangeably, the combination of Artificial Intelligence and Machine Learning can be particularly beneficial within the asset management industry. In short, AI is a broad catch-all term that describes the ability of a machine – usually a computer system – to act in a way that imitates intelligent human behaviour. By contrast, Machine Learning is the study of the algorithms and methods that enable computers to solve specific tasks without being explicitly instructed how and instead of doing so by identifying persistent relevant patterns within observed data.
Professionals in the asset management industry need to be able to predict the future effectively if they are to be successful and generate wealth for their investors, which explains why experts see Artificial Intelligence and Machine Learning as the most potentially disruptive technology for the industry in the coming 3 to 5 years.
Investment predictions, like all predictions, are made by combining information with a model or method along with the assumption that managers have some advantage which allows them to make predictions that are more likely to be right than wrong. The human ability to beat the market is waning, which leads to the failure of managers to generate promised returns. A key reason for this is the continued reliance on the same old information and methods used by investment managers to make decisions, along with the increased speed of change in the markets, which allows for models to become obsolete much more quickly than in the past.
Fortunately for us, advanced AI offers a solution to this dilemma. Machines have access to an infinite number of trading opportunities, so the models are constantly adapting to market trends, subsequently making them more dynamic and risk averse. As 2021 looks likely to be another year of increased market volatility - machines will continue to be on the lookout for great opportunities throughout all points of the market cycle. We must not forget that the most significant challenge faced by investors is uncertainty. With AI, the uncertainty is simply handed to an algorithm where the predictions and timings are completely automated. The influence of emotional and cognitive bias can thus be incrementally removed over time, making the use of AI and machine learning a much better solution for the long and short-term.
Overall, asset managers must continue to develop systematic and scientific investment processes in order to generate sustainable risk adjusted returns for their trading activities. The use of AI can industrialise the invention process of trading models, and in doing so, outsmart the human mind. If we have learnt one thing from 2020, it is that asset management industries can reap substantial benefits through the implementation of AI and machine learning.
Google launches Visual Inspection AI tool for manufacturers
Google Cloud has launched Visual Inspection AI, a new tool to help manufacturers identify defects in products before they're shipped.
Poor production quality control often leads to significant operational and financial costs. The American Society for Quality estimates that for many organisations this cost of quality is as high as 15-20% of annual sales revenue, or billions of dollars annually for larger manufacturers. Google Cloud’s new Visual Inspection AI solution has been purpose-built for the industry to solve this problem at production scale.
How does it work?
The Google Cloud Visual Inspection AI solution automates visual inspection tasks using a set of AI and computer vision technologies that enable manufacturers to transform quality control processes by automatically detecting product defects.
Google built Visual Inspection AI to meet the needs of quality, test, manufacturing, and process engineers who are experts in their domain, but not in AI.
- Run autonomously on-premises: Manufacturers can run inspection models at the network edge or on-premises. The inspection can run either in Google Cloud or fully autonomous on your factory shop floor.
- Short time-to-value: Customers can deploy in weeks, not the months typical of traditional machine learning (ML) solutions. Built for process and quality engineers, no computer vision or ML experience required. An interactive user interface guides users through all the steps.
- Superior computer vision and AI technology: In production trials, Visual Inspection AI customers improved accuracy by up to 10x compared with general-purpose ML approaches, according to benchmarks from several Google Cloud customers.
- Get started quickly, with little effort: Visual Inspection AI can build accurate models with up to 300x fewer human-labeled images than general-purpose ML platforms, based on pilots run by several Google Cloud customers.
- Highly scalable deployment: Manufacturers can flexibly deploy and manage the lifecycle of ML models, scaling the solution across production lines and factories.
Industry use cases
The demo video shows how Visual Inspection AI addresses use cases to solve specific quality control problems in industries such as automotive manufacturing, semiconductor manufacturing, electronics manufacturing and general-purpose manufacturing.
Kyocera Communications Systems, a manufacturer of mobile phones for wireless service providers, has been able to scale its AI and ML expertise through the use of the solution. “With the shortage of AI engineers, Visual Inspection AI is an innovative service that can be used by non-AI engineers,” said Masaharu Akieda, Division Manager, Digital Solution Division, KYOCERA Communication Systems. “We have found that we are able to create highly accurate models with as few as 10-20 defective images with Visual Inspection AI. We will continue to strengthen our partnership with Google to develop solutions that will lead our customers' digital transformation projects to success.”
Visual Inspection AI has fully integrated with Google Cloud's portfolio of analytics and ML/AI solutions, giving manufacturers the ability to combine its insights with other data sources. The tool integrates with existing products from Google Cloud partners, including SOTEC, Siemens, GFT, QuantiPhi, Kyocer and Accenture.