Artificial Intelligence (AI) has come a long way from the early models to something that is actually being commercialised in a way that it can be in the mainstream.
We have seen a lot of refinement when it comes to Artificial Intelligence (AI) and Machine Learning (ML) development but there is still a long way to go.
This is because the thing about Artificial Intelligence (AI) is that the more developed it gets the more we have to think about important questions regarding its implementation.
There are moral and ethical challenges as well as technological challenges that need to be sorted out before we can move to the next stage of AI development.
This discussion is important because these challenges can pose a roadblock for this otherwise billion and nearly trillion-dollar industry by the beginning of the 2030s.
We truly believe AI has the power to pave the road for amazing advancements to our entire human civilization but before that, we need to solve some important concerns about Artificial Intelligence (AI).
That is why this blog will help you understand these important challenges and we will also try to provide our own version of solutions and how these challenges can be overcome.
While we are not cleaning that our solution is the very best, at least it is going to start conversations so that the outcomes are thought about.
So, here we go, here are some of the most important current AI challenges.
Important AI Challenges and Our Solutions to Solve Them
AI Ethics
Challenge
One of the biggest concerns about Artificial Intelligence (AI) that anyone can have been the ethical issues associated with AI. The thing about AI is that AI makes it possible to generate data artificially.
That might be good in most use cases but that’s dangerous in others.
We are talking about things like Deep Fakes as well as things like creating data for malicious purposes and it is actually happening just as you speak.
Things can get even more serious if you talk about areas of important decision-making that have a serious impact on human life and society and we are talking about areas of law and order as well as the judiciary and the healthcare industry.
However, the thing with AI is that it is so new in the mainstream that there are not really any kind of laws in place to regulate AI.
While AI platforms try to prevent the generation of malicious and offensive content, there is a limit to what they can do if there are no concrete laws out there to regulate the industry.
This is one of the biggest challenges when it comes to AI is ethics and the use of AI.
Solution
The only solution to this is international cooperation and governments sitting down at international conventions to make universal laws that will be applicable throughout the world.
This is because if you go through the route of asking individual nations to pass legislation then the foundation of the AI ethics laws will be very shaky.
If individual countries have different laws for AI ethics, then the bad elements can just operate from another country with no ethics laws.
That is why the only solution is international cooperation regarding this matter of utmost importance.
AI Biases
Challeng
The second challenge we are going to talk about is unavoidable to an extent and we are talking about biases that might be produced because they were unintentionally introduced during the training stage of the AI models.
Every AI model has to be trained with a set of data regarding different topics and that set of data might be biased towards certain groups or towards a certain outcome. That happens sometimes just because the datasets contain more of a certain type of data.
For example, if you look at this AI image upscaling application. The model is given an image with very few pixels. We all know it’s the image of Barack Obama, a person of African-American ethnicity. Now look at the results the AI has produced. The results completely changed the ethnicity of Barack Obama into that of a white Caucasian male.
Is AI not at fault because the model simply learnt from datasets that were convoluted in the first place and the datasets from this particular application probably had more white faces as training material.
The actual problem begins when these AI models are used for tasks like filtering out the resumes of people during automated hiring procedures as well as the selection of loan applications and approvals.
If the AI model is biased towards a group of people during the training, then it will make judgements based on those biases and it can have an impact on the lives of real people.
Solution
Now only solution to this is for AI engineers to be much more selective aware it comes to providing training datasets to models for training.
They just need to be much more careful of the fact that Artificial Intelligence (AI) does not know what is good or bad and it is up to them to teach the AI to be impartial.
However, if we are being genuine with ourselves then there is no solution to this kind of problem because there will always be biases because it’s impossible to have perfect datasets with the perfect kind of ethnic and demographic representation.
AI Integration
Challenge
Integrating AI into your existing systems can prove very valuable for operations automation as well as for improving the entire efficiency of your business.
But there is a challenge to this because while AI might have developed into what we know today, it has still not reached the ultimate level of evolution when it comes to integration.
This means that you might have excellent AI models that are already passing the bar exams and taking part in important decision-making making but we are still a little way away from easy integration with existing systems.
This is a challenging roadblock that is preventing the inclusion of AI in the business operations of companies.
Solution
The solution to this is actually funding initiatives to make AI much more accessible to small, medium and large businesses and also individuals.
While AI might be very cheap at the moment, it should be completely free for individual use and quite affordable for business use.
Whenever you want a technology to succeed and become mainstream, you have to make sure it is affordable and accessible and that is also the way to AI integration.
If funding is provided to companies that actively encourage AI integration, then things can be quite different.
AI Computing
Challenge
One of the biggest challenges of developing any AI model is the requirement for a lot of computing power in the form of GPUs and TPUs and this is not only very energy intensive but it is also very costly.
This is a problem because not everyone can simply go out there and afford the very best Tensor chips and start developing their AI model. They can use regular hardware but it will take a lot of time to develop that model.
The reason for this is that when you are training AI, it has to process a lot of information and it all depends on how much you can compute if you want to reduce that processing time.
Even if you want to carry on the development of already powerful models, the development requirements get much more intensive with each iteration of AI improvement.
Solution
The solution to this is the creation of affordable hardware that is only created for AI applications and while that will slowly but surely happen in the future, it will only happen when AI gets much more mainstream.
Private entities as well as governments need to pitch in to help with AI hardware development funding.
These specialised pieces of hardware can be made compatible out of the box with regular systems and this will enable every company to implement these specialised hardware and create specialised AI models.
That’s not to say that such technologies do not exist. However, they are most AI accelerators or small modules not capable of much computation.
One such hardware is the USB accessory AI Accelerator Module developed by Google.
Data Privacy
Challenge
If you want to train your AI model then you need to provide it with a lot of varied datasets and information for the purpose of training because AI models need a lot of data in order to get trained.
That is where the problem lies because if you want to get your hands on a lot of data and do it ethically then that can be challenging because every piece of data has its own set of copyright laws associated with it.
Getting that data is not easy and that might be a challenge for companies who want to maintain the ethics of data collection and also train a good AI model.
For example, if you remember the clash of Scarlett Johansson with OpenAI and its founder over the use of her voice in the AI assistant ChatGPT then you exactly how complicated data laws can be.
Solution
The only solution feasible for these kinds of data laws is for the government to come out with new data laws that will allow AI models to be trained with data much more easily so that the transfer and usage of data is easier.
If governments do not take care of this then AI platforms cannot simply train their AI models ethically because they will have to spend more of their time talking to their lawyers from all the lawsuits they get for using unauthorised data. Or they will have to release statements trying to justify data use.
While we understand the importance of copyright laws, it is also important to sometimes make some kind of fundamental exceptions for the betterment of humanity and make data available for public use.
Governance and Regulation
Challenge
One of the challenges for any new technology that may bring generational change is with rules, regulations and governance.
Think about the time when YouTube was launched, the developers had to go through a lot of trials and issues in order to respect copyright policy so that it did not infringe on anyone’s rights regarding the handling of copyright.
Well, that is also the case with AI because you need a lot of data when it comes to training any AI model and there is a challenge of governance and regulation in their area.
You must also think about the fact of utilisation of AI-generated content because that can also lead to governance issues.
Solution
The solution is for private players to work hand in hand with the government and create committees and benches.
However, the problem with these committees is that oftentimes they do not include experts. If you do not include AI engineers and experts in the legal discussions then things will not move ahead.
That is why every party responsible for AI and governance should be included in discussions and new laws need to be created for not only the use of AI material but also the determination of areas where AI cannot be used.
For example, the usage of AI in the judiciary, education, healthcare, defence applications, psychology etc.
AI Transparency
Challenge
One of the biggest challenges for artificial intelligence is what we call the Black Box phenomenon. We understand how to make AI work but we can’t really see the kind of logic AI models use to do that work.
All we can do is control the input, look at the output and create the algorithm in between. We are simply not able to access the logic behind how the AI model works and what goes on in the decision-making process of the AI model.
This is a challenge because unless we know exactly how something works, we can’t really master that technology no matter how hard we try.
Solution
The solution to this is finding a way in order to understand the underlying logic behind the decisions taken by AI models.
One such way can be the development of Explainable AI. They are a set of approaches that can allow humans to ask questions regarding the decision-making logic behind an output.
While the development of this right kind of approach is at its very early stages of development, the only significant thing to do is to fund AI research that encourages tools and approaches like this towards transparency.
Whenever this happens, the logic-making process behind AI outputs will enable us to trust and utilise AI much better.
Security
Challenge
Imagine a situation where you are trying to develop an AI model by training it and hackers get access to it during the training process and they try and introduce biases into the system.
They hide their own convoluted data within your datasets and this will not only corrupt the entire model but it has the potential to virtually create a dangerous AI model.
However, let us not go too far with this because any kind of attack on AI can be very dangerous because of how powerful AI is.
AI in the wrong hands is very dangerous.
Solution
There are multiple kinds of solutions to this complex challenge because the first solution is utilising encryption methods that not only protect the AI model but also the datasets.
You can also look at a solution in the form of better data storage as well as monitoring systems that are there to find out if there is any tempering to the AI model.
You can also find a solution in the form of collaboration with industry leaders when it comes to cybersecurity and by employing ethical hackers in your AI development company to look for vulnerabilities.
As an AI engineer, you can also try and make stricter AI usage guidelines so that scammers do not use your model for bad purposes.
International Cooperation
Challenge
One of the biggest challenges with AI is knowledge sharing and research collaborations with countries from different continents.
If you know the condition of geopolitics right now then you will understand how difficult it is to create any kind of trade agreement let alone knowledge sharing agreement and standardisation of technology policy.
Even if you get some countries to agree to it, some countries will simply not agree to your AI policy just because they are not in the best of terms with some of your partners.
Political agreement is very difficult but of course not impossible.
Solution
The solution to this is the United Nations(UN) because if you want to actually achieve something then you need a body or an organisation with its members consisting of every country in the world.
You can also try passing resolutions through different global organisations and bodies of cooperation such as the EU or the BRICS and other bodies.
You can only come to a point of an international corporation when the demand for this resolution reaches the highest echelons of power.
Job Displacement
Challenge
The introduction of any kind of technology brings with it a lot of challenges, especially any kind of technology that has the potential to drastically improve the efficiency of different processes.
That is also the case with Artificial Intelligence (AI) because AI has the power to not only do simple tasks but actually complicated tasks that would have needed a human employee.
That makes things really complicated when it comes to human and AI cooperation as well as the point about AI taking away jobs.
If you come across a situation like that then people will never be quite comfortable with the idea of AI.
The thing we need to understand is that AI is never going to take average the jobs of humans because humans will always have to stay in a position of supervision.
Solution
The solution to making people understand that this is actually not an issue and that this false sense of paranoia is not new.
Is to tell them about the example of when computers were just getting introduced into the mainstream.
When computers were being introduced, people also had the notion that it was going to take away all jobs but something completely different happened.
Computers created more jobs and the jobs they did always needed human supervision not to mention the rise of an entirely new manufacturing industry for computer hardware. So, in essence, computers actually created more jobs than they took away.
AI is going to create more jobs just as computers did and all you need to do is to tell people about these arguments so that they do not worry too much about AI.
We hope this blog helps you understand some of the most significant challenges of Artificial Intelligence (AI) as well as our possible solutions to these challenges.
We are Think To Share IT solutions and we are experts at AI integration and implementation as well as AI Innovation and we would love to implement AI into your existing business operations and improve the efficiency of your business.
We welcome you to visit our website and check out everything we do.
