AI Blog Series

What are some of the challenges involved in implementing AI and machine learning technologies?

Introduction AI and machine learning have become buzzwords in the world of business and technology. They are also among the hottest trends in the technology industry, with many major companies investing heavily in AI research projects. Despite this interest from some key players, there are still many challenges standing between businesses and their AI ambitions. To get started with AI or ML projects, you need access to high-quality data sets that can be used to train machine learning models. This can prove difficult if your company doesn't have an extensive customer database or isn't involved in industries like telecommunications or retail where there is more readily available data about customers' preferences and buying habits.
Machine learning is one of the most exciting and powerful applications of artificial intelligence. The training phase, where the model learns from a set of labelled data, can be computationally expensive. This is because it requires many iterations over the data to determine how well its predictions match those of the ground truth labels. It's also because there are so many possible choices for determining what factors should be included in the model and how they should interact with each other; no single best combination exists out of all these possibilities.

It's important to emphasize that this training process must take place on extremely large datasets (e.g., millions or even billions of samples) if you want accurate results. If your dataset isn't big enough, then it will fail at predicting accurately on future examples outside its training set—and that's no good for an AI system!

In addition to increasing accuracy through more data, having more data also allows you to build more complex models (i.e., models that have more parameters), which often lead themselves to better performance metrics.
Limited access to high-quality labelled training data. Data is the new oil. If you want to get anywhere in the world of AI and machine learning, you need to be able to access high-quality labelled training data. The problem is that data is a resource that's in relatively short supply, and it can be expensive to acquire. This makes it challenging for companies without an established data strategy (and even those with one) to build out AI products or services quickly enough for market demands.

It's not just about acquiring the right amount and type of data; there are other considerations as well: ensuring that your company has proper policies around data governance, security, privacy and ethics; understanding who owns what kinds of information; managing relationships between different groups on campus who might have their own ideas about how certain information should be used; ensuring compliance with relevant regulations such as GDPR or HIPAA; making sure all parties involved understand how they're contributing towards achieving common goals...
Market scepticism and lack of understanding amongst potential customers. Market scepticism and lack of understanding amongst potential customers.

AI and machine learning technologies are still relatively new to the market, which is why many companies don't know what they can do with them. As such, they're uncertain as to whether AI is worth investing in or not. Additionally, many people have not yet fully grasped what these technologies actually mean; therefore they're also unsure about their benefits and capabilities.
Uncertainty over whether AI will deliver ROI to the business. Many organizations are still questioning the viability of AI and machine learning technologies. In many cases, organizations are unsure if this technology will provide a return on investment (ROI) or create value for their business.

There are also many questions about how AI will impact society in the long term. There is a lack of understanding about whether or not these technologies will become widespread and what impact they could have on users as they become more involved with everyday life. Some researchers worry that consumers may become too dependent on AI applications; others believe that these systems could make it easier for people to live longer lives by helping them manage their health conditions better than ever before!
ML models don't require explicit programming to arrive at their results, they learn by example. When you train a machine learning model, it learns by example. In other words, you teach the model how to classify data into different categories by feeding it examples of what those classifications look like. Once you've done this, these classifications become the basis for making predictions about new data that might not have been seen before.

This process is known as training the model. When we say that a model has been trained on a dataset, we mean that it has been fed all of (or at least most of) that dataset's inputs and outputs so that it can make predictions based on them later on (i.e., when asked).

Once a model has been trained on enough examples from its input domain—the set of potential inputs for which it will make predictions—it can be tested against new data from outside its input domain to see how well it performs overall: did our algorithm accurately predict whether or not someone was likely to buy something online? Did those who bought products online tend to come back again? And so forth...
No clear channel for AI-driven sales and marketing. AI has become a buzzword in the tech world and is often thought of as a solution to all problems, but it’s important to remember that AI is more of an enabler than a solution. AI will not be able to replace human sales and marketing efforts any time soon. While AI can help automate repetitive tasks and improve efficiency, it can’t replace the human touch when it comes to customer relationships and lead generation.
Reluctance of senior management to invest in AI research projects in face of uncertainty. Senior management buy-in is critical to the success of any AI research project. Without it, there's little chance that you'll be able to convince your colleagues and peers to invest their time and energy in an experiment with high stakes. The more senior a manager is, the more difficult it will be for them to recognize the risks involved with implementing AI technologies. If they understand how these technologies work and their potential benefits, then they'll feel more comfortable investing in them. They may even act as advocates for future projects!

If you want your company's senior executives on board with your vision for future technology use cases—and if you want them excited about what could happen if those visions come true—you need to take some time getting acquainted with their perspectives about risk management versus reward maximization strategies (or whatever strategy fits within your organizational structure).
Lack of expertise in-house in AI, ML and Data Science. To successfully implement AI, you need to have a team of data scientists who can understand your data and translate it into actionable insights. Data scientists are critical for AI projects because they have the skills necessary to build algorithms, test and refine them, and identify ways in which they can be used.

To find and recruit data science talent, you'll need to do some research on what types of skills are needed for this emerging field (e.g., knowledge of machine learning or artificial intelligence). You'll also need to assess how much experience candidates have with these technologies so that you don't waste time training them on foundational concepts like linear regression or deep learning when they already know it inside-out.
Insufficient cloud infrastructure to develop, test and deploy machine learning models. Insufficient cloud infrastructure to develop, test and deploy machine learning models.

The cloud provides the flexibility needed to iterate quickly on machine learning models, but only if you have the right tools in place. If you don't have enough cloud infrastructure for your data scientists or developers, they're not going to be able to do what they need. They won't be able to build their own applications on top of existing frameworks in order to facilitate these iterations—and they'll likely hit a wall where their workloads are too heavy for existing resources.
In order to succeed with AI, Businesses must find ways to counter these challenges When implementing AI, it is important to address these challenges. To do so, businesses must first develop a clear understanding of the business problem they are trying to solve with AI and how it fits in their broader business strategy. They also need to have a clear understanding of their target audience and how they can use their data effectively. Finally, they need to understand what type of data they have and where it comes from before building an AI solution that works for them.
Conclusion "We need to train people to manage AI, not just use it. The real value of AI is its ability to interpret data and make decisions on our behalf; in this regard, humans are still the most powerful machines on earth." - Andrew Ng
profile Tim Davies
5 min read