Have you seen videos online showing eerily human-like robots marching around or machines beating pros in chess and Go? Google‘s artificial intelligence division DeepMind takes this a leap further in their quest to mimic human athleticism. In a new research paper, they reveal an AI system rapidly learning to walk, run, dribble, pass, and shoot soccer balls into goals. How is this possible and what does it mean for the future of technology and sports?
The Backstory: DeepMind‘s Cutting-Edge AI Research
Founded in 2010 and acquired by Google in 2014, DeepMind has been at the forefront of AI safety and ethics research. They made headlines in 2016 when their AlphaGo program beat the world champion in the complex board game Go. The company has focused on training AI agents to excel in simulations and games with the goal of eventually translating these learnings into real-world benefits.
Co-founder Demis Hassabis has stated their overarching mission is:
"solving intelligence to advance scientific discovery for all of humanity’s benefit."
Could digitally simulated soccer advance this lofty vision? That‘s exactly what DeepMind explores in their latest paper.
The Purpose: Eventually Benefitting Pro Athletics
While DeepMind is clearly still in early research stages, they outline purposes extending far beyond just beating human soccer stars:
"In this paper, we provide an overarching perspective highlighting how the combination of these fields, in particular, forms a unique microcosm for AI research while offering mutual benefits for professional teams, spectators, and broadcasters in the years to come."
The key phrase there is "mutual benefits." Rather than aiming to outright replace human athletics, DeepMind envisions collaborations where AI systems provide data, analytics, injury predictions, personalized training, tactics analysis and more to complement world-class coaching staffs and players.
Imagine Cristiano Ronaldo wearing biometric sensors with real-time data streamed to an AI able to analyze his every move and vitals in match situations. This technology could optimize training routines down to the most minute details.
While we‘re still years away from realizations like that, the trail DeepMind is blazing toward those future collaborations marks incredibly exciting progress at the intersection of sports, technology and AI.
The Technique: Virtual Trial-and-Error Learning
So just how did DeepMind take those first strides toward teaching AI to play authentic soccer? Their approach centers around trial-and-error learning simulations. These physics-based environments confront the AI "players" with many real-world challenges:
- Collisions with other players
- Slippery turf that leads to falls
- Errant passes and kicks
- Gravity that causes loss of balance
- Balls reacting unpredictably
The researchers began by rendering humanoid forms with the same general physical capabilities as professional athletes. These virtual players started off struggling with basics like walking or running without falling:
Years Simulated | Skills Attained
0-1.5 years | Balancing to stand, ginger steps
1.5 - 10 years | Walking, jogging, running
10-15 years | Dribbling balls
15-20 years | Shooting at targets
20+ years | 2 vs 2 games with passesTo accelerate learning, the researchers utilized motion capture data from videos of real soccer matches. This allowed the AI players to analyze and attempt mimicking the movements of pro athletes.
After its first 1.5 simulated years, the AI could jog around the field reasonably well. By year 10, it developed dribbling abilities. After 15 years, shooting and passing skills started emerging. And by year 20, full games of 2 vs 2 involving team coordination began spontaneously occurring in the simulations.
Pushing The Boundaries in Physical Robotics
In tandem with these humanoid soccer simulations, DeepMind is conducting experiments focused on simpler tasks like pushing balls towards targets with physical robot bodies. While seemingly basic, controlling limbs and motors to interact precisely with real world objects poses immense challenges fundamentally different from virtual environments.
The skills learned in simulation do appear to translate over and allow the robot AI to adapt relatively quickly. Researchers report the physical systems master skills like aiming, kicking and maneuvering around obstabcles within an hour of simulation training that would have previously taken several hours of real-world practice to match.
DeepMind suggests these approaches could extend into areas like basketball, tennis, boxing and other sports depending on available motion capture data sources. The core techniques around what they call "neural probabilistic motor primitives" form fundamental building blocks applicable far beyond just soccer robots.
Current Limitations to Physical Soccer Players
While DeepMind‘s virtual simulation progress seems remarkably fast, we are still likely many years away from android players passing the ball around World Cup pitches. Some fundamental barriers remain to be solved before that vision becomes reality:
Ability to Learn from Watching
Human players improve quickly through visual observation alone. Watch Messi dribble past defenders for hours and your brain intuits moves to attempt replicating. AI systems still struggle profoundly with this method of skill acquisition. They cannot purely watch video and self-improve.
The simulated agents instead needed thousands of years of physics-based trial-and-error within the virtual environment to refine techniques. This constraint severely limits the pace and ceiling of learning – a core bootstrapping problem that persists as an open research challenge across AI communities.
Translating Simulation to Reality
Reinforcement learning within high-fidelity physics simulations allows rapid trial-and-error experimentation impossible in the real world. However issues still arise around translating those virtual skills into real-world physical systems. Factors like friction, minute details of movement sequences and speed of execution prove extremely difficult to simulate with complete accuracy.
And simulations inevitably remain lower visual quality than real sensors, posing challenges for visuomotor control. So while the basics may transfer from simulation training to physical systems in limited drills, competitive 11 vs 11 soccer contains infinitely more complexity.
The Future: Towards AI-Human Collaboration in Sports
Based on DeepMind‘s paper and demonstration videos, fully replacing human soccer stars like Megan Rapinoe or Sadio Mané with AI players does not seem viable in the foreseeable future. However these research strides do suggest technological shifts on the horizon around relationships between athletes and artificial intelligence.
Rather than pure competition, we may see new paradigms of collaboration emerge – elite players leveraging shared strengths with AI to unlock untapped potential. This vision is highlighted in the paper‘s stated goal around “mutual benefits.”
One can certainly raise reasonable concerns around reliance on data-centric AI in training and tactics. Over-optimization risks draining creative flair and improvisation from the beautiful game. Rule changes may prove necessary to govern integration of technologies like biometric tracking, computer vision analysis and embedded motor control. Clear ethical standards will help steer innovations toward expanding possibilities while tempering unwanted effects.
If cultivated responsibly though, human-AI cooperation in sports could yield a wealth of enhancements: ultra-customized coaching, real-time guidance during matches, accelerated rehabilitation, sustained career longevity and more. Exciting to imagine Ronaldo donning an exosuit that allows playing into his 50s through minor boosts imperceptible to spectators!
Concluding Thoughts on This Remarkable Technology
The virtual AI players in DeepMind‘s paper remain firmly simulations requiring eons of training time. However their work represents remarkable strides crossing into exciting territory at the intersection of sports, robotics and artificial intelligence.
While competitive android leagues are not imminent, this research clearly demonstrates machines making progress in unlocking the secrets behind extraordinarily complex realms of human movement. And beyond just replicating our biological faculties, fusion of unique strengths between both sides opens doors to as yet unimagined possibilities enhancing athletics.
If mom warned playing video games all day would rot your brain, DeepMind‘s paper offers an emphatic counterpoint. Their simulated games just taught AI systems to walk, run, kick, head and dribble balls like professional soccer stars!
