Dr Abdelrahman AlMahmoud is the principal cloud and big data researcher at Technology Innovation Institute, a UAE-based scientific research centre
January 03, 2022
I was optimistic a year ago that AI would aid us considerably in putting an end to the pandemic. As an international community, however, we are still struggling to stem the spread of Covid-19 with all of its evolving variants, the latest named Omicron.
When the pandemic started, hundreds of AI projects were announced around the world; from infection-tracking systems to technologies that claimed to reverse engineer the code of the virus, to means that accelerated the speed of vaccine discovery, to processes that quickly diagnosed Covid-19 from medical images.
Medical staff prepare a patient in need of an ECMO (extracorporeal membrane oxygenation) life support unit for a CT examination, at the coronavirus disease (COVID-19) Intensive Care Unit in Darmstadt, Germany, December 11. Reuters
I often get asked where all the promise of AI went. In some people’s minds, the hype around AI remains unfulfilled or exaggerated. In fact, according to some estimates, around 85 per cent of AI projects will fail. However, it is unfair to gauge the success of AI based on a single project’s failure or success. On the contrary, we should measure success based on how big of an impact AI had or will have on certain domains and on a positive global effect on the pandemic.
I recall seeing hundreds of face mask detection projects when the pandemic started. While some have tried to spinoff startups from the technology, these startups tried to capitalise on the need for mask enforcement during the pandemic. None of them are in use right now. It turned out that this wasn't as big of a deal as many imagined and policies and law handled it much more effectively.
Another popular application was diagnosing Covid-19 from medical images.
Initially, many thought that medical staff would not be able to cope with the huge number of tests requested by people, considering we had millions of infections worldwide. Enter deep learning tools that could detect positive cases in seconds from x-rays and MRIs.
In theory, these were the perfect solution. However, it turned out that taking imaging devices like MRI machines into the field was impractical. Setting up a mobile lab to collect swabs made much more sense.
A health worker in a mobile lab takes a sample for a Covid-19 test in New Delhi, India. AP
Furthermore, these imaging systems could not be used in hospitals. They needed further clinical studies and scrutiny by regulatory authorities to ensure they were safe for medical use – tests that not many ended up passing. Thus, only a select few of these diagnostic tools are actually still in use.
AI is tied to available data. Unfortunately, the ability to access relevant, real-time data is extremely privileged. Only the top tech company superpowers and governments have access to granulated data. While the tech companies have a good handle on the data framework – due to heavily monetising it – governments still struggle to build such a framework. In fact, several governments around the world have struggled to collect useful data because of the extremely high level of expertise needed. Add to that the privacy issues, and there is a definite challenge on your hands.
If we want effective AI solutions, then we must define a framework for researchers, governments and the private sector
The existing data monopoly often strangles smaller companies and startups and pushes them to work on less pressing problems, such as face mask detection. The solution to this is very complex, due to data sharing frameworks and the nature of personal data.
One popular approach has been to share small datasets with researchers to test and develop their systems. While that can get the ball rolling, it is extremely difficult to do it in a meaningful way. In most cases, such efforts only end up being useful for student course projects or to test a hypothesis. The reality is that building effective AI tools requires constant development, monitoring and on flowing data which can only be achieved with tight integration. Static datasets just do not suffice in the real world.
Let us also look at the example of Tesla, one of the leaders in autonomous driving – the concept of driver-less cars has gripped many imaginations over the years. In pursuit of making this goal a reality, Tesla is effectively crowdsourcing data from its enormous fleet of cars to its cloud and data centres. The amount of engineering work, expertise and massive infrastructure developed to cope with incoming data is an example of the huge effort needed to solve these challenges.
A new Tesla owner demonstrates on a closed course in Portland, Oregon, how he can play video games while driving, on December 8, 2021. The US has opened an investigation into a report that Tesla vehicles allow people to play video games on a centre touch screen while behind the wheel. AP
To put a dent in a global healthcare challenge like Covid-19 requires much more effort than a quick-win mentality. The time, effort and resources needed is not something that a single entity – even tech giants – can handle. What is needed is government support, funding and the integrated effort of a top scientists and their teams.
If we want effective AI solutions, then we must define a framework for researchers, governments and the private sector to get access to relevant data when needed. The first steps towards addressing these challenges are already under way in the form of analysis systems that preserve privacy and are secure, but much remains to be done still to make them practical.
Finally, isolation is the biggest impediment to AI success, by which I mean the disconnect between academic institutions and governments. If you have attended any meeting where researchers and officials are trying to engage with one another, you will immediately see that these teams tend to speak different languages. They simply do not understand one another.
Or in many cases, their interests, the way they perceive the challenges, and their thoughts on how to proceed don't align.
While they can both do just fine in their own bubbles, neither will be able to enjoy any significant impact or transformation. It is time to re-engage all parties to drive this worthwhile effort in a more organised and consistent way.
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Match info
Deccan Gladiators 87-8
Asif Khan 25, Dwayne Bravo 2-16
Maratha Arabians 89-2
Chadwick Walton 51 not out
Arabians won the final by eight wickets
Iraq negotiating over Iran sanctions impact
US sanctions on Iran’s energy industry and exports took effect on Monday, November 5.
Washington issued formal waivers to eight buyers of Iranian oil, allowing them to continue limited imports. Iraq did not receive a waiver.
Iraq’s government is cooperating with the US to contain Iranian influence in the country, and increased Iraqi oil production is helping to make up for Iranian crude that sanctions are blocking from markets, US officials say.
Iraq, the second-biggest producer in the Organization of Petroleum Exporting Countries, pumped last month at a record 4.78 million barrels a day, former Oil Minister Jabbar Al-Luaibi said on Oct. 20. Iraq exported 3.83 million barrels a day last month, according to tanker tracking and data from port agents.
Iraq has been working to restore production at its northern Kirkuk oil field. Kirkuk could add 200,000 barrels a day of oil to Iraq’s total output, Hook said.
The country stopped trucking Kirkuk oil to Iran about three weeks ago, in line with U.S. sanctions, according to four people with knowledge of the matter who asked not to be identified because they aren’t allowed to speak to media.
Oil exports from Iran, OPEC’s third-largest supplier, have slumped since President Donald Trump announced in May that he’d reimpose sanctions. Iran shipped about 1.76 million barrels a day in October out of 3.42 million in total production, data compiled by Bloomberg show.
Benchmark Brent crude fell 47 cents to $72.70 a barrel in London trading at 7:26 a.m. local time. U.S. West Texas Intermediate was 25 cents lower at $62.85 a barrel in New York. WTI held near the lowest level in seven months as concerns of a tightening market eased after the U.S. granted its waivers to buyers of Iranian crude.
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List of alleged parties
May 15 2020: PM and Carrie attend 'work meeting' with at least 17 staff members
May 20 2020: PM and Carrie attend 'bring your own booze' party
Nov 27 2020: PM gives speech at leaving do for his staff
Dec 10 2020: Staff party held by then-education secretary Gavin Williamson
Dec 13 2020: PM and Carrie throw a flat party
Dec 14 2020: London mayor candidate Shaun Bailey holds staff party at Conservative Party headquarters
Dec 15 2020: PM takes part in a staff quiz
Dec 18 2020: Downing Street Christmas party
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