The Importance of MES in Harnessing Manufacturing Data for AI and ML Solutions

Without the tools to reason about mistakes a model is making in the wild, teams are investing a massive amount of money in the data science laboratory but essentially flying blind in the real world. We would like people to be aware of the realistic risks of AI and what can happen when it goes wrong. Kate Crawford and Vladan Joler have previously shown how much labour, resources and data is involved in the Anatomy of an AI System. And having a better understanding of how the AI works in a product or system might help people use it more effectively, maybe giving them more agency in the process. Explaining your AI systems might also differentiate something from competitors and help it stand out. Understanding AI better might help an organisation improve its AI-based systems or products.

Explaining artificial intelligence

• Some platforms will be upward-compatible; however, others may not.
• Beyond that we’ll write about some of the projects that BBC R&D is developing to help explain artificial intelligence.
• Some businesses could even make the entire system controllable with a digital voice assistant.
• We would like people to have a realistic understanding of the capabilities and promises of AI.
• Explaining your AI systems might also differentiate something from competitors and help it stand out.

Systems that employ ML aren’t magic and their application can use conventional design approaches. They do require new tools and debugging techniques, so incorporating ML for the first time shouldn’t be a task taken lightly. On the other hand, the payback can be significant and ML models may often provide support that’s unavailable with conventional programming techniques and frameworks. A key question facing business leaders today is why do some companies fare so poorly with their ML initiatives while others succeed? To understand how and why some machine learning and AI initiatives fail, look no further than the evolution of software development. Although ride-sharing and video advertising aren’t often used in the same sentence, both Jason and I faced similar challenges in ensuring that the models our companies deployed worked effectively and without bias.

Easy Automation with MOVI‑C®

For example, a model trained to recognize cats and dogs may be able to provide a high level of confidence that an image contains a dog or a cat. The level may be lower distinguishing a dog from a cat and so on, to the point that a particular breed of animal is recognized. Training for some types of neural networks requires thousands of samples, such as photos. This is often done in the cloud, where large amounts of storage and computation power can be applied. Trained models can then be used in the field, since they normally require less storage and computation power as their training counterparts. AI accelerators can be utilized in both instances to improve performance and reduce power requirements.

Continuous Torque Drives Peak Performance

A number of factors can be recorded and analyzed from power provided to the motor to noise and vibration information. Different tools or ML models can be used to identify areas of interest that are then isolated and processed to distinguish between objects such as people and cars. Most ML models can be trained to provide different results using a different set of training samples. For example, a collection of cat photos can be used with some models to help identify cats. I hope that those who have been working with ML take kindly to my explanations, because they’re targeted at engineers who want to understand and use ML but haven’t gotten through the hype that even ML companies are spouting.

• It is a method by which machines improve their performance over time using data generated during production.
• But it’s important to remember that these programs take time and require a clear strategy and roadmap.
• They might have concerns about AI and explaining how it works might assuage these, or it might increase their trust in a system.
• DNNs are just a part; other neural-network approaches enter into the mix, but more on that later.
• My co-founder Jason previously led video ad company TubeMogul (acquired by Adobe), which relied on ML to ensure that its advertisers didn’t waste their media spend on ads that nobody saw, or ads that only bots saw.
• Read on to see how AI and ML can improve efficiency, performance and the employee experience.

You could use a smart security system and cameras to protect the property and employees. Many of these devices can be programmed to work together for higher levels of automation. Some businesses could even make the entire system controllable with a digital voice assistant.

Developers can narrow the search for the ideal model by specifying the memory footprint and latency time. Models can perform different functions such as detection, classification and segmentation. Other functions could include path optimization or anomaly detection, or provide recommendations.

How ML can solve root cause application failure mysteries for engineering and support teams

ML is only a part of the AI field and many ML tools and models are available, being used now, and in development (Fig. 1). DNNs are just a part; other neural-network approaches enter into the mix, but more on that later. With complexities of applications and environments continually increasing and demands on support organizations mounting, introducing ML for logs to the application support process is quickly moving from a luxury to a necessity. When a production issue occurred, they could easily see the problem.

And just as software-enabled digital transformation became the province of CEOs, understanding the inner workings of AI and ML initiatives has entered the domain of business leaders. In any data-driven business, how ML works and how it can drive a positive ROI are now questions that should be understood by the C-suite. The promise of ‘General AI’ is that we can make machines that think like humans (and indeed become more “intelligent” than us). The media portrayal of AI, particularly in fiction, is often along these lines. The reality of AI now is that it is used to try to solve very specific, narrow problems like recommending TV programmes or generating plausible-sounding sentences. But these methods don’t generalise to human-level intelligence (whatever that might mean!).

At the same time, remedying them is getting more complex as features grow and dependencies on things like software microservices and cloud infrastructure proliferate. The six-hour Facebook outage in October 2021 resulted in losses of $164,000 per minute and cut the company’s market cap by some $40 billion. The December 2021 AWS outage wreaked havoc across the U.S.  Banks, service companies and other retailers suffer considerable losses when mobile apps or web applications fail.

They wanted a dashboard that would show what they need to do to complete their job, while not forcing them to become data analysts. As a result, the VP said his team would have to spend countless hours diving into the data to figure out the root cause to any problem. This pulled them away from the shop floor where he wanted them to spend more time, making sure quality product was produced.

But understanding why the issue occurred was not easily seen. All their reports and dashboards also didn’t tell them how the problem could be prevented next time. Read on to see how AI and ML can improve efficiency, performance and the employee experience. In the next part we’ll look at some different ways of explaining and some different places where we could intervene in the world to explain AI better.

We also focus on creating the foundation to help correctly implement new technologies in the future. Our digital transformation team is made up of experts in automation and controls, MES and DataOps. We believe bringing an experienced cross functional team together provides our clients not only value but de-risks their initiatives. At the BBC we use AI in our services and products; recommending programmes to you, suggesting news articles to read, or even for compressing video. DNNs have been popular because of the availability of open-source solutions, including platforms like TensorFlow and Caffe.