How to overcome AI and machine learning adoption barriers
There has been a considerable amount of hype around Artificial Intelligence (AI) and Machine Learning (ML) technologies in the last five or so years.
So much so that AI has become somewhat of a buzzword – full of ideas and promise, but something that is quite tricky to execute in practice.
At present, this means that the challenge we run into with AI and ML is a healthy dose of scepticism.
For example, we’ve seen several large companies adopt these capabilities, often announcing they intend to revolutionize operations and output with such technologies but then failing to deliver.
In turn, the ongoing evolution and adoption of these technologies is consequently knocked back. With so many potential applications for AI and ML it can be daunting to identify opportunities for technology adoption that can demonstrate real and quantifiable return on investment.
Many industries have effectively reached a sticking point in their adoption of AI and ML technologies.
Typically, this has been driven by unproven start-up companies delivering some type of open source technology and placing a flashy exterior around it, and then relying on a customer to act as a development partner for it.
However, this is the primary problem – customers are not looking for prototype and unproven software to run their industrial operations.
Instead of offering a revolutionary digital experience, many companies are continuing to fuel their initial scepticism of AI and ML by providing poorly planned pilot projects that often land the company in a stalled position of pilot purgatory, continuous feature creep and a regular rollout of new beta versions of software.
This practice of the never ending pilot project is driving a reluctance for customers to then engage further with innovative companies who are truly driving digital transformation in their sector with proven AI and ML technology.
Innovation with direction
A way to overcome these challenges is to demonstrate proof points to the customer. This means showing how AI and ML technologies are real and are exactly like we’d imagine them to be.
Naturally, some companies have better adopted AI and ML than others, but since much of this technology is so new, many are still struggling to identify when and where to apply it.
For example, many are keen to use AI to track customer interests and needs.
In fact, even greater value can be discovered when applying AI in the form of predictive asset analytics on pieces of industrial process control and manufacturing equipment.
AI and ML can provide detailed, real-time insights on machinery operations, exposing new insights that humans cannot necessarily spot. Insights that can drive huge impact on businesses bottom line.
AI and ML is becoming incredibly popular in manufacturing industries, with advanced operations analysis often being driven by AI. Many are taking these technologies and applying it to their operating experiences to see where economic savings can be made.
All organisations want to save money where they can and with AI making this possible.
These same organisations are usually keen to invest in further digital technologies. Successfully implementing an AI or ML technology can significantly reduce OPEX and further fuel the digital transformation of an overall enterprise.
Understandably, we are seeing the value of AI and ML best demonstrated in the manufacturing sector in both process and batch automation.
For example, using AI to figure out how to optimize the process to achieve higher production yields and improve production quality. In the food and beverage sectors, AI is being used to monitor production line oven temperatures, flagging anomalies - including moisture, stack height and color - in a continually optimised process to reach the coveted golden batch.
The other side of this is to use predictive maintenance to monitor the behaviour of equipment and improve operational safety and asset reliability.
A combination of both AI and ML is fused together to create predictive and prescriptive maintenance. Where AI is used to spot anomalies in the behavior of assets and recommended solution is prescribed to remediate potential equipment failure.
Predictive and Prescriptive maintenance assist with reducing pressure on O&M costs, improving safety, and reducing unplanned shutdowns.
Both AI, machine learning and predictive maintenance technologies are enabling new connections to be made within the production line, offering new insights and suggestions for future operations.
Now is the time for organisations to realise that this adoption and innovation is offering new clarity on the relationship between different elements of the production cycle - paving the way for new methods to create better products at both faster speeds and lower costs.
Google is using AI to design faster and improved processors
Engineers at Google are now using artificial intelligence (AI) to design faster and more efficient processors, and then using its chip designs to develop the next generation of specialised computers that run the same type of AI algorithms.
Google designs its own computer chips rather than buying commercial products, this allows the company to optimise the chips to run its own software, but the process is time-consuming and expensive, usually taking two to three years to develop.
Floorplanning, a stage of chip design, involves taking the finalised circuit diagram of a new chip and arranging the components into an efficient layout for manufacturing. Although the functional design of the chip is complete at this point, the layout can have a huge impact on speed and power consumption.
Previously floorplanning has been a highly manual and time-consuming task, says Anna Goldie at Google. Teams would split larger chips into blocks and work on parts in parallel, fiddling around to find small refinements, she says.
Fast chip design
They have created a convolutional neural network system that performs the macro block placement by itself within hours to achieve an optimal layout; the standard cells are automatically placed in the gaps by other software. This ML system should be able to produce an ideal floorplan far faster than humans at the controls. The neural network gradually improves its placement skills as it gains experience, according to the AI scientists.
In their paper, the Googlers said their neural network is "capable of generalising across chips — meaning that it can learn from experience to become both better and faster at placing new chips — allowing chip designers to be assisted by artificial agents with more experience than any human could ever gain."
Generating a floorplan can take less than a second using a pre-trained neural net, and with up to a few hours of fine-tuning the network, the software can match or beat a human at floorplan design, according to the paper, depending on which metric you use.
"Our method was used to design the next generation of Google’s artificial-intelligence accelerators, and has the potential to save thousands of hours of human effort for each new generation," the Googlers wrote. "Finally, we believe that more powerful AI-designed hardware will fuel advances in AI, creating a symbiotic relationship between the two fields.