- This article was first published by Sports Pundit on the 14th November 2023 and republished by Zone7 with permission from the author.
In the ever-evolving realm of artificial intelligence (AI), an intense competition is unfolding among the tech industry’s titans, each striving for supremacy.
As the majority of eyes remain fixated on AI ‘performance’ (i.e. the accuracy and relevance of answers or outputs), another crucial yet often understated battleground is emerging—the race to secure access to invaluable data, the lifeblood of AI model training.
OpenAI, xAI, and Amazon are among those companies at the forefront of this battle, all employing their own distinct approaches to attain this lifeblood.
Earlier this month, for instance, OpenAI announced the ability for ChatGPT Plus subscribers to create custom AI agents with their GPT platform. This no-code solution opens the door for a multitude of applications and tasks, while OpenAI gathers an extensive range of proprietary data in the process.
Alternatively, Amazon unveiled advanced AI capabilities for its Alexa products, powered by a model aptly named AlexaLLM. The technology will make Alexa “more personalised to your family” and allow it to remember relevant context throughout conversations like a human. In the process, the retail giant is potentially tapping into an enormous wealth of highly sought-after behavioural data.
It’s also crucial to consider xAI’s approach to the data sourcing problem as well.
Elon Musk recently unveiled Grok, a chatbot designed for X (formerly Twitter) users, boasting a sense of humour reminiscent of his own (you can decide if that’s a good or bad thing). However, what really sets Grok apart is its “real-time access” to information from the X platform.
“If you think Elon’s access to the fire hose of human consciousness which is X/ Twitter is valuable, then logically the extension would be that Grok has a very serious edge here.”Trung Phan (Co-Host, Not Investment Advice Podcast)
In this high-stakes competition, the age-old adage, “garbage in, garbage out,” couldn’t be more relevant. However, this push for quality data also needs to be tempered with an understanding that quantity of data also plays an indispensable role in an AI model’s development.
As these competitors will all recognise, the more high-quality data an AI model can access, the more accurate and powerful it can become.
Drawing from my own experience, this is precisely why high-performance organisations seek partnerships with companies like Zone7. There is no way that their own datasets in silo could provide forecasts as accurate as models that have been trained on millions of hours of training data from across numerous professional teams.
In fact, Zone7 have repeatedly heard from clients, particularly those in highly developed data environments (such as in Premier League and NBA), that in contrast to scouting data, which can be purchased at scale from companies like Statsbomb, individual performance data both on and off the field is both difficult to clean and not available in the necessary scale (think many thousands of athletes covered) for usage within predictive models.
What this tells us is that amid all the likes of OpenAI and Amazon fighting to dominate general intelligence, there remains space for specialist AI companies with exclusive access to high-quality proprietary data, to create unique, industry-specific benefits. This is particularly true when also combined with a profound understanding of a specific environment.
General AI, exemplified by ChatGPT, cannot rival the nuanced expertise that specialist models offer. In the realm of highly specific vertical tasks, such as performance data analysis in professional sport, the capabilities of ChatGPT would fall short.
While it may be able to craft a rudimentary training plan, it would lack the contextual sophistication demanded by a high-performance organisation. For example, it’s not going to have, or be able to take into account, insights which are hyper contextualised to athlete’s medical and performance profiles or to the team’s schedule and training philosophy.
So, why pen this article? Well, in a world that is soon-to-be dominated by AI, individuals, teams, and organisations within the sports industry need to recognise the true worth of both their data and environmental context, as well as the short falls if they are relying on general AI.
Equally significant is the acknowledgment that much of the value of this data that they are generating and collecting comes from being part of a collective, as opposed to solitary actors.
No single team or entity can conquer AI alone. For the sports industry to truly benefit from the advancements in AI, the sharing of data and expertise is the only path to victory and progress.
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