Why self-driving vehicles need shared data platforms
With the likes of disruptive car (or should that be tech?) companies such as Tesla and more recently, Nikola, eclipsing established manufacturers in terms of value, vehicle automation represents a chance to re-level the playing field - hence why it’s not just the likes of Google-affiliate Waymo making strides in the space. Toyota, for instance, has invested heavily in an autonomous vehicles startup.
While the eventual goal is an autonomous vehicle at level 5 of the Society of Automotive Engineers’ (SAE) Levels of Driving Automation Standard, representing complete autonomy at all times, that target remains a way off, with most vehicles only exhibiting partial automation.
Actually bringing these cars to market will require a number of factors to align. Consumer confidence, for instance. There have been fatal incidents involving the technology, but also numerous examples of autonomous vehicles avoiding catastrophe.
Perhaps an even more fundamental aspect of a successful transition to autonomous vehicles is freely available mapping data, levelling the playing field for all comers. A newly published report from Zenzic has outlined just that.
The report found that the currency existing fragmentation of geospatial mapping fata is a roadblock to the global development of self-driving vehicles. The report recommended creating industry-wide standards on mapping requirements in the areas of data formats; data quality and resolution; terminology; minimum safe requirements and standards; government data and traffic regulation orders; and data hosting.
Daniel Ruiz, CEO, Zenzic said in a press release: “This report shows that the global self-driving vehicle development industry agrees that mapping data needs to be easily shareable for us to achieve the goal of having self-driving vehicles on our streets by 2030. When it comes to the maps which will form the basis of how self-driving vehicles see the world, the details matter, from how this data is shared, to what resolution of mapping data is deemed safe. The UK is well placed to lead the development of standards and regulation as organisations like OS and BSI have done some for decades.”
AI Shows its Value; Governments Must Unleash its Potential
2020 has revealed just how far AI technology has come as it achieves fresh milestones in the fight against Covid-19. Google’s DeepMind helped predict the protein structure of the virus; AI-drive infectious disease tracker BlueDot spotted the novel coronavirus nine days before the World Health Organisation (WHO) first sounded the alarm. Just a decade ago, these feats were unfathomable.
Yet, we have only just scratched the surface of AI’s full potential. And it can’t be left to develop on its own. Governments must do more to put structures in place to advance the responsible growth of AI. They have a dual responsibility: fostering environments that enable innovation while ensuring the wider ethical and social implications are considered.
It is this balance that we are trying to achieve in the United Arab Emirates (UAE) to ensure government accelerates, rather than hinders, the development of AI. Just as every economy is transitioning at the moment, we see innovation as being vital to realising our vision for a post-oil economy. Our work in his space has highlighted three barriers in the government approach when it comes to realising AI’s potential.
First, addressing the issue of ignorance
While much time is dedicated to talking about the importance of AI, there simply isn’t enough understanding of where it’s useful and where it isn’t. There are a lot of challenges to rolling out AI technologies, both practically and ethically. However, those enacting the policies too often don’t fully understand the technology and its implications.
The Emirates is not exempt from this ignorance, but it is an issue we have been trying to address. Over the last few years, we have been running an AI diploma in partnership with Oxford University, teaching government officials the ethical implications of AI deployment. Our ambition is for every government ministry to have a diploma graduate, as it is essential to ensure policy decision-making is informed.
Second, moving away from the theoretical
While this grounding in the moral implications of AI is critical, it is important to go beyond the theoretical. It is vital that experimentation in AI is allowed to happen for its own sake and not let ethical problems stymie innovations that don’t yet exist. Indeed, many of these concerns – while well-founded – are born out in the practical deployment of these end-use cases and can’t be meaningfully discussed on paper.
If you take facial recognition as an example, looking at this issue in abstract quickly leads to discussions over privacy concerns with potential surveillance and intrusion by private companies or authorities’ regimes.
But what about the more specific issue of computer vision? Although part of the same field, the same moral quandaries do not arise, and the technology is already bearing fruit. In 2018, we developed an algorithmic solution that can be used in the detection and diagnosis of tuberculosis from chest X-rays. You can upload any image of a chest X-ray, and the system will identify if a person has the disease. Laws and regulations must be tailored to unique use-cases of AI, rather than lumping disparate fields together.
To create this culture that encourages experimentation, we launched the RegLab. It provides a safe and flexible legislation ecosystem to supports the utilisation of future technologies. This means we can actually see AI in practice before determining appropriate regulation, not the other way around. Regulation is vital to cap any unintended negative consequences of AI, but it should never be at the expense of innovation.
Finally, understanding the knock-on effects of AI
There needs to be a deeper, more nuanced understanding of AI’s wider impact. It is too easy to think the economic benefits and efficiency gains of AI must also come with negative social implications, particularly concern over job loss.
But with the right long-term government planning, it’s possible to have one without the other; to maximise the benefits and mitigate potential downsides. If people are appropriately trained in how to use or understand AI, the result is a future workforce capable of working alongside these technologies for the better – just as computers complement most people’s work today.
We’ve to start this training as soon as possible in the Emirates. Through our Ministry of Education, we have rolled out an education programme to start teaching children about AI as young as five years old. This includes coding skills and ethics, and we are carrying this right through to higher education with the Mohamed bin Zayed University of Artificial Intelligence set to welcome its first cohort in January. We hope to create future generations of talent that can work in harmony with AI for the betterment of society, not the detriment.
AI will inevitably become more pervasive in society, digitisation will continue in the wake of the pandemic, and in time we will see AI’s prominence grow. But governments have a responsibility to society to ensure that this growth is matched with the appropriate understanding of AI’s impacts. We must separate the hype from the practical solutions, and we must rigorously interrogate AI deployment and ensure that it used to enhance our existence. If governments can overcome these challenges and create the environments for AI to flourish, then we have a very exciting future ahead of us.