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The Bright Future of AI - What's Next?

Image by Gerd Altmann from Pixabay.

If you read my previous article, you may recall that some even blame the Turing test for limiting our goals when it comes to AI. They say that the test has given us a narrow-minded focus, where we limit AI’s capabilities by pitting it against human intelligence.


Let's start with healthcare. Healthcare has undergone a variety of changes in recent years, forever improving as technology does so too. In 2018, the Secretary of Health Matt Hancock said that AI ‘will play a crucial role in the future of the NHS’. Following this, the UK Government shared its goal for the prevention, early diagnosis, and treatment of chronic illnesses to be completely transformed by the world of AI and data in 2030. And that’s not all. Accenture claims that hospitals will invest $6.6 billion a year into AI-related tech by 2021. Furthermore, Safavi and Kalis estimated that ‘AI applications could create up to $150 billion in annual savings for US healthcare by 2026’.

So the future for AI in the healthcare industry is looking bright. Everyone has a keen eye for it. But what about some practical applications? Turns out, it’s not all entirely watch this space. It is being used in the industry at the moment, it’s just a work in progress. Grady Hospital in Atlanta reported that they saved $4 million due to the application of an AI tool that identified ‘at risk’ patients. The money was saved because the tool resulted in a 31% reduction in readmission rates over two years. Addenbrooke hospital in Cambridge has been using Microsoft’s InnerEye system to automatically process scans for patients with prostate cancer. The system will outline the image, mark up the tumors and report it back. The hospital admitted that they are considering using this technology for brain tumor patients also. Furthermore, HeartFlow’s AI tech is being used to analyze CT scans of patients suspected to have coronary heart disease, creating a personalized 3D model to help doctors identify where the blood flow is being blocked. This actually cuts the usual costs of such a task by ¼ when compared to the usual angiogram procedure which is used.


Similarly to the healthcare industry, the transport industry also has a keen eye on Artificial Intelligence. The European Parliament produced a report on the possibility of using AI. According to them, it has most successfully been applied to road transport, where a variety of manufacturers worldwide are looking into using AI to develop automated vehicles based on various sensors such as GPS, camera, and radar.

Case in point, Ford channeled $1 billion into their Argo AI project to create a self-driving car. More well known is Uber’s ambitious goal to create a self-driving taxi. However, this goal proved to be a tad too ambitious and was fraught with controversy. First of all, the company was sued by Waymo for allegedly stealing Google’s trade secrets. Anthony Levandowski was sentenced to 18 months in jail. Furthermore, Elaine Herzberg tragically lost her life at the hands of one of Uber’s self-driving vehicles in March of 2018. Recently, Uber sold this project to Aurora Innovations.

It is expected that global AI in the transport market will reach $3.5 billion by 2023, with a growth rate of 16.5%. There are a number of places it could be used, including traffic management, sustainable transport, and even drone taxis. It has even been noted that AI is slowly taking over traffic lights, which technically work not by AI, but by sensors. To read more about AI in traffic lights, be sure to check out this amazing blog by Samaniego.


Did you know that in 1999, AI flight software controlled a spacecraft for not only one but several days? Yeah, neither did I. The AI was called Remote Agent Experiment (RAX), and it was uploaded to Deep Space One to control the DS-1 mission. The experiment was a success, paving the way for the use of artificial intelligence up above and even gaining itself an award as the co-winner of the 1999 NASA Software of the Year Award.

Its success gave birth to the Mixed-Initiativestone model Activity Planning Generator (a tactical planning system used for the Mars exploration rovers, aptly named MapGen) and the Living stonemodel-based diagnosis engine, which is currently part of the discussions for system-health management aboard the Crew Exploration Vehicle (CEV).

Another example of AI in space is the Autonomous Sciencecraft Experiment (ASE) which was deployed in 2003 and is aboard the Earth-Observing One (EO-1) spacecraft. If you’d like to hear more about ASE you can do so here.

Disaster Management

AI has also been making tremendous improvements in disaster risk reduction. A study shows that the most common use of AI in the detection and forecasting of natural disasters between 2018 and 2021 was for floods, standing at 34.50%, followed by earthquakes at 21.8% and landslides at 17.4%. Its least common use was for Avalanches, which stood at 0.9%.

Due to a large amount of data available to us from a variety of sources, it is expected that AI will gain a more prevalent role in disaster risk reduction (DRR). In Georgia, the United Nations Development Programme (UNDP) is in the process of creating a nationwide multi-hazard early earning system. Due to the taxing demands of the project, experts are using AI to create a tool that will preduce the probability of observing connective events under specific conditions.

When Japan’s devastating Tohuku Tsunami hit, it took several days to fully note the damage. If these observations were to be mixed with observations from AI systems to assess and evaluate threats and impact as they occur, this time could be minimized by a significant amount. The experts of the Global Navigation Sattelite Systems have begun to consider the possibility of using AI to process their data, specifically in countries where exporting real-time data is prohibited by law.

You can’t deny that if their idea were to go ahead, many properties and perhaps even lives could be saved. There is no denying that the prohibition of real-time information being shared could be detrimental in situations, and if Artificial Intelligence were to provide an apt loophole to this issue, it would certainly be beneficial. TiiQu believes that information should be shared and available to all.

And on that note, I’d say that’s a nice place to leave it here. I'm sad to say that my time looking at AI has reached it's end. Here at TiiQu, no information is hidden, and as forth I took it upon myself to provide something new every month. Next week I'll be looking at Climate Change, but keep an eye on TiiQu and Qpidia, because although my time looking at AI is over, AI will never stop. It has a vast future, and you should be sure to check out the variety of content available as we grow and learn, just like AI will.

  • Marr, B., 2021. 4 powerful examples of how AI is used in the NHS. Bernard Marr. Available at: [Accessed September 20, 2022].

  • Lee, D.H. & Yoon, S.N., 2021. Application of artificial intelligence-based technologies in the healthcare industry: Opportunities and challenges. MDPI. Available at: [Accessed September 20, 2022].

  • Hawkins, A.J., 2020. Uber's fraught and deadly pursuit of self-driving cars is over. The Verge. Available at: [Accessed September 20, 2022].

  • Chauhan, D., 2022. Artificial Intelligence in transportation: Moving faster toward the future. Stefanini. Available at:,impressive%20growth%20rate%20of%2016.5%25. [Accessed September 22, 2022].

  • Chien, S. & Davies, A.G., 2006. The Future of AI in Space. Available at: [Accessed September 22, 2022].

  • Kuglitsch, M. et al., 2022. Artificial Intelligence for Disaster Risk Reduction: Opportunities, challenges, and prospects. World Meteorological Organization. Available at:,situational%20awareness%20and%20decision%20support%2C [Accessed September 22, 2022].

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