Artificial Intelligence (AI) and Machine Learning (ML) are among the most promising fields for the future of technology. A 2021 study by Markets and Markets showed that the AI industry is expected to grow from its current market size of $58 billion to $310 billion by the year 2026. So, what does this mean for the future of machine learning and AI in product development?
Machine learning uses data and algorithms to make predictions. Machine learning algorithms use historical data as inputs to make predictions without being explicitly programmed to do so. There are a few different types of machine learning that are applicable to product design:
Supervised machine learning is a subcategory of machine learning and artificial intelligence. It uses labeled datasets to train algorithms and classify data or predict outcomes accurately.
Unsupervised machine learning is another subcategory of machine learning. It uses machine learning algorithms to analyze and cluster unlabeled datasets. These algorithms discover hidden patterns or data groupings without needing human intervention.
Semi-supervised machine learning is a combination of supervised and unsupervised machine learning methods. In these instances, a small amount of labeled data can be used with a large amount of unlabeled data to improve learning accuracy.
ML system design is the process of defining, creating and optimizing neural networks. Neural networks are a series of algorithms that are used to identify and assess vast amounts of data to discover underlying relationships. These patterns allow for predictions on the most optimal response.
ML systems churn through massive amounts of data. Traditionally, these models have been created and run in cloud servers. However, now, computer scientists and engineers are pushing ML even further. They are designing deep neural networks and embedding them directly within their systems processors. These inbuilt processors are able to run AI and ML inferences locally without needing a cloud or server connection.
Artificial Intelligence (AI) is a broad field whose goal is to create machinery that simulates human behavior and intelligence. Machine Learning (ML) is a branch of AI and computer science which refers to software that is able to learn from past data in order to predict outcomes.
Those working as pioneers in ML set out to create software that can adequately assess past scenarios and pinpoint causes of various effects. They then use the insights generated from these models to predict future challenges and prepare countermeasures accordingly.
Yes. Many companies today see a growing need to embed ML in every step of the product development process. A few examples include:
According to MIT Technology Review Insights, 71% of manufacturers have seen a 5% or more increase in revenue from adopting machine learning product design. This indicates that the future of machine learning is bright. ML is expected to continue to help product managers understand their existing products better and know exactly what to focus on while building the next generation of products. This data could range from sales, production, quality to cost. Let’s take the automotive industry as an example. Advancements in machine learning have been used by engineers and data scientists for:
AI in product design is being used to answer fundamental systems design questions such as how different materials would work together in the making of a vehicle. In these situations, engineers don’t need to wait to see how well a new alloy would work. Instead, they can estimate the alloy’s tensile strength and predict its reaction to prolonged exposure to the elements.
AI is being used to test autonomous vehicles and control systems. Car manufacturers can now simulate end-to-end systems including:
Car manufacturers are simulating these AI algorithms over millions and even billions of miles to gain confidence that their car can meet the strict regulation standards. This situation is not unique to the companies developing autonomous vehicles such as Tesla, Cruise and Uber. Most new vehicles already have embedded control systems that use advancements in machine learning. Examples of these systems include cruise control and lane keeping.
ML is being used extensively to optimize production and inventory levels. ML models have been introduced to many manufacturing facilities as a way to maximize their Overall Equipment Effectiveness (OEE). OEE is a measure of how well a manufacturing line is utilized compared to its full potential during the periods it is scheduled to run.
As an example, BMW recently won the Connected Car Award. It uses AI and ML in its Dingolfing plant to compare the live image of a model coming down the production line with order data. If that does not match (e.g., if the xDrive is missing), the workers who are carrying out the final inspection receive a notification to investigate further.
Any step as impactful as the step towards AI in product development is bound to have costs and risks to its implementation. We will attempt to cover these costs and risks:
Software based solutions are vulnerable to software based attacks. A survey and machine learning interviews conducted by McKinsey and Company on the State of AI in 2021 showed that cybersecurity remains the biggest risk among AI product management practitioners. Therefore, companies that employ AI as part of their product must ensure top notch security.
Fully autonomous vehicles and robots that use AI in product development are especially vulnerable. Companies cannot allow their products to succumb to any bugs or attacks from malicious parties as that could affect the safety of human beings. This means that those products must endure thorough testing, verification and validation before moving into the production phase.
Machine learning models that will spearhead the future of artificial intelligence will rely on huge amounts of data. Despite recent improvements in synthetic data creation and labeling, it is still not on par with real world data. Therefore, real world data is important have. This data can be obtained in many ways and companies have seen increased scrutiny on how they acquire and use this data.
Some instances of companies collecting data when consumers aren't aware have come out in the news recently. And in many cases, users would rather not send their data to third parties despite the improvement that it can have on their user experience. It is therefore important that companies are compliant with regulations, such as GDPR, about the information they collect.
ML systems design requires vast amounts of processing power to be accurate enough to solve real world problems. Though computing spend depends on the use case, in general, AI product development is an expensive affair. And many companies find it difficult to keep up with the costs of running 1000s of hours of compute and the complexity of provisioning servers. As a result, it is not uncommon for companies to outsource these requirements to other companies.
As vehicles and other autonomous systems become more sophisticated, the future of product development requires modeling and simulation tools that match the need. At Collimator we strive to do just that.
Collimator is an engineering tool for data driven modeling and simulation of dynamical systems. Collimator is the only tool that allows you to import big data directly via API, design your system using neural networks from TensorFlow or PyTorch, and simulate millions of test cases using HPC in the cloud so perception and control engineers can work off of a single source of truth.
Find out more about how Collimator can help you build your ML based system by booking a meeting with our application engineering team.