Dr Kjell Carlsson: Data is the largest untapped resource available to firms today
Dr Kjell Carlsson will be set to speak at Quant Strats 2024 at 1.50 pm on the PANEL: Where can ML realistically outperform humans? The panel discussion will take place on March 12 at the Quorum by Convene, New York.
Please give us a little introduction to your current role and what you do
Both in my current role, and when I was an industry analyst at Forrester Research, I advise companies on building their AI and ML capabilities and driving impact at scale using these technologies. I also host the Data Science Leaders podcast where I interview executives and other thought leaders on AI best practices, trends, and key topics, such as GenAI and Responsible AI.
What has been you /your firm’s top 3 priorities for the coming year?
- Responsible AI - helping customers implement the array of capabilities they need to govern, monitor, audit, and ensure fairness and reliability across the AI development and deployment lifecycle, which they need in order to scale the adoption, and resulting impact, of AI.
- Generative AI - helping customers orchestrate the ecosystem of new technologies and services they need to operationalize cutting-edge, specialised GenAI applications responsibly and at scale.
What do you think are the biggest challenges facing data scientists/AI experts/quantitative practitioners/portfolio managers for 2023 and beyond?
Scaling AI - the biggest challenge remains moving from ad hoc, “artisanal” production of AI and ML models to “industrial” scale, ie dramatically increasing the development and operationalization of these models everywhere the company has data. For most companies, this involves creating new AI leadership roles, implementing a “system of systems” to orchestrate the ecosystem of AI technology components, and building “industrial-grade” processes for managing and governing the AI lifecycle.
With some semblance of stability returning to the US and European markets – where do you think the next source of Alpha is?
Data is the largest untapped resource available to firms today for driving alpha and AI/ML is making it faster to drive new, monetizable insights from data, especially unstructured data, than ever before.
What is the biggest potential economic banana skin that could shock the system?
Geopolitical risk is at an all-time high, but beyond that, there is a significant risk that companies are going to fail in their AI, especially GenAI, initiatives, not because of the technologies themselves, but because they lack the people, process, and platform capabilities necessary to achieve the potential return on these investments.
With Gen.Ai being the word on everyone's lips and vigorous debate about the impact it has and will have on financial markets – do you think that it will seismically change the landscape or will it just automate the ‘boring stuff’
GenAI will lead to seismic changes but for a relatively narrow set of use cases. For example, it is revolutionising the field of drug discovery when it comes to protein-based treatments, and disrupting outsourced customer service. Beyond these GenAI “killer applications” it will be automating the “boring stuff” in processes like never before and we can expect to be using GenAI-based features in our daily lives as often as (perhaps even more often than) we use internet search today.
The data market seems to be incredibly saturated at present, with more data being available than at any other time in human history – do novel data sources still exist?
While there will always be novel data sources, the incredible new opportunity right now is in leveraging AI on existing unstructured data (text, images, video, voice, logs) and for extracting signal across data sources, at scale more easily and (when done effectively) more accurately than ever before. Effectively, AI is generating usable “novel” data from the existing, largely untapped universe of unstructured data.
Is the value now in merging different data sets together to give a ‘full picture’? And what challenges does this bring?
The value lies in applying AI and ML both to extract signals across ever more datasets and to combine these signals in new predictive models. However, to take advantage of this most firms need to dramatically upgrade their AI capabilities and processes.
Is the crypto winter coming to an end? And can quant be applied to the digital asset space or is there too much regulatory divergence?
No, AI has sucked the oxygen from competing emerging technologies like blockchain and quantum computing where there haven’t been significant disruptive innovations. There will still be a large market for crypto for its existing use cases of speculation and facilitating illegal payments, but there is little reason to predict that it will grow dramatically beyond this.