12 a-list celebrities with a degree in engineering
From Hollywood to production line, Manufacturing Global unveils 8 celebrities who have studied engineering. The list may surprise you.
You will know them best for donning red carpets in Los Angeles or starring in hit TV shows and films but these A-list celebs also have a hidden talent – a degree in engineering. The Queen Elizabeth Prize for Engineering in London has shared a list of celebrities who have studied engineering to highlight the sector, and the likes of Cindy Crawford, Ashton Kutcher and Donald Sutherland make the list.
Impressed? We certainly were.
Celebrities in engineering
- Cindy Crawford studied chemical engineering at Northwestern University, Illinois, before taking to the catwalk.
- Teri Hatcher, studied mathematics and engineering at De Anza College, California, before playing Lois Lane in The New Adventures of Superman and later, Susan in Desperate Housewives.
- Ashton Kutcher studied biochemical engineering. While the subject may have not have prepared him for his acting career - aside perhaps from playing Steve Jobs - it may have stood him in good stead for his interest in tech start-ups. Kutcher has so far successfully invested in companies such as Foursquare Skype, Airbnb, Spotify and yPlan.
- Swedish Actor Dolph Lundgren who is known for his roles in action movies, has a degree in chemical engineering from the Royal Institute of Technology in Stockholm, followed by a master's degree in chemical engineering from the University of Sydney in Sydney
- Before becoming the 108th Mayor of New York City and a business magnate, Michael Bloomberg studied electrical engineering.
- Actor Donald Sutherland, the son of an engineer, has a degree in chemical engineering from the Royal Institute of Technology in Stockholm.
- Footballer Dennis Bergkamp studied mechanical engineering at University of Bath.
- Scott Adams, creator of Dilbert, was a software and telecommunications engineer, before drawing his famous comic strips.
- Tom Scholz, founder of rock band Boston, studied mechanical engineering at MIT and holds a number of patents.
- Herbie Hancock, a jazz musician, studied electrical engineering.
A number of celebrities have expressed an interest in become more technically-minded too, signaling greater interest in the industry.
- Will.i.am has said that he would like to study computer science and has recently launched a smart watch.
- Model Karlie Kloss, recently participated in ‘Hour of Code’ to encourage people to learn how to create their own computer programmes.
Despite more people keen to build their own apps and a widespread interest in trends such as green technology there is a major shortage of engineers. In the UK alone, 1.82 million new engineering, science and technology professionals will be needed by 2022.
The Queen Elizabeth Prize for Engineering, which has been likened to a ‘Nobel’ for engineering, aims to raise the public profile of engineering. The £1 million prize has this afternoon been awarded to chemical engineer Robert Langer for his revolutionary advances in the fields of chemistry and medicine.
5 Minutes With PwC on AI and Big Data in Manufacturing
Please could you define what artificial intelligence is, and what Big Data is?
AI is the ability of a machine to perceive its environment and perform tasks that normally require human intelligence, and it’s a whole field of different technologies, techniques and applications.
Big data is a set of tools and capabilities for working with, for processing, extremely large sets of data.
How does AI and Big Data work together?
Big data is just one of the enablers of AI, though as we see increasing volumes of data, it’s one of the most important
How can this be applied to a manufacturing setting?
Broadly speaking, there are many benefits of AI, and the use of data, which include reducing costs, minimising human error, and increasing productivity and efficiency. The important thing to consider is any setting - for the use of any technology - is what is the problem you are trying to solve? Be it merely automating repetitive tasks or to reinventing the nature of work in factories by having humans and machines collaborate in order to make better and faster decisions.
Why should manufacturers use AI and Big Data when adopting smart manufacturing capabilities, what is the value for manufacturers?
One view is, again, the economic benefits of AI, which come in manufacturing as a result of:
1. Productivity gains from automating processes and augmenting the work of existing labour forces with various applications of AI technologies.
2. Increased consumer demand due to the increased ability to personalise and tailor manufactured products, along with higher-quality digital and AI-enhanced products and services.
Manufacturing (and construction industries) are by nature capital intensive, and in our 2018 report, “The potential impact of AI in the Middle East,” we estimated that the adoption of AI applications could increase the sectors’ contribution to GDP gains by more than 12.4% by 2030.
How can AI and Big Data help manufacturers to evolve in the Industry 4.0 revolution? What about those already looking at Industry 5.0?
It’s really about the investment you make now, in order to futureproof your business.
We typically see two broad strategies or approaches to the adoption of AI. There are things that we can do immediately, without any recourse to Big Data - which is to adopt technologies we describe as Sensing, those involving computer vision, for example. There are plenty of use cases where these can be used immediately in manufacturing, such as for automatic fault detection. However, there is a longer term play which requires investing in data - getting the right collection mechanisms in place, storage, data governance, Big Data capabilities etc - in order to develop increasingly valuable machine learning driven AI use cases. This is absolutely necessary for long term adoption success.
What is the best strategy for organisations looking to realise the value of AI and Big Data in manufacturing?
AI and Big Data are only one part of a successful smart factory. The organisations that lead on AI adoption are those who have already made the most progress in digitising core business processes. In order get ahead in using AI solutions at scale, there are a number of technology investments and organisational decisions to be made, including:
1. Digitising processes ultimately leads to improved ability to generate data, and in the manufacturing setting - with many 100s of sensors generating 1000s of measurements in real time, the result is Big Data. Data is key to building AI so reliable and accurate data acquisition, management and governance are key. The production line and factories play a critical and direct role in the data-acquisition process.
2. AI strategy, both long and short term, begins with the use cases, the business applications. Manufacturers need to ask where they want to use AI and gather these use cases together and prioritising projects based on a balance of expected impact and complexity of implementation.
Of course, in addition to technology and business processes, people are at the heart of any successful technology adoption. AI teams need to be composed not only of data scientists, also data engineers and solution architects to enable their work, data stewards to ensure accuracy, and increasingly so call “Analytics/AI translators” who are able to communicate with business leaders and technology experts. Culture is also key, and manufacturers need to enable a data and AI-driven culture, building trust in data and algorithms by educating their workforce about AI and its capabilities, how best to extract value. It’s not just the positive of course, but also the risks and limitations, as these when encountered without expectations having been set, can significantly impact willingness to invest.
What are the challenges when it comes to adopting AI and Big Data in manufacturing?
has shown that one of the major challenges to implementing AI is uncertainty around return on investment (ROI). As I said, there is significant investment required for a long term data and AI strategy to be successful, and expectations around the time to see tangible returns must be set realistically.
Many companies also struggle with the data side: collecting and supplying the data that an AI system needs to operate, and ensuring that it is accurate. Again, this speaks to the bigger investments required in digitisation.
Some of the main challenges for manufacturing companies with implementing AI at a scale from our research include:
- 40% → Technologies not mature
- 40% → Workforce lacks skills to implement and manage AI
- 36% → Uncertain of return on investment
- 33% → Data is not mature yet
- 32% → lack of transparency and trust
- 24% → Work councils and labour unions
- 22% → Regulatory hurdles in home & important markets
One element highlighted here, particularly around lack of trust, and labour unions, is that AI is typically misrepresented in the media as “replacing” workers, and taking jobs. Yes, there are efficiency gains to be made from automation, as there have been since the first industrial revolution. But we believe that Data and AI are at their most valuable when they are used to augment workers, enhancing their abilities and the products being manufactured.
Another challenge we’re starting to see emerge is cyberattacks increasingly targeting interconnected equipment and machinery in smart factories. PwC recently hosted a webcast, in cooperation with the National Association of Manufacturers in the US and Microsoft to discuss this.
What are the current trends in AI and Big Data in manufacturing?
- We see companies putting slightly more focus on adding AI solutions to core production processes such as the engineering, and assembly and quality testing
- Safety is of significant importance, with techniques adopted in protocol adherence capabilities (for example maintaining safe distance from specific machinery) being adopted in more every day scenarios for COVID-19 protocol adherence
- There is considerable interest in predictive maintenance for large machinery involved in manufacturing processes, and also supply-chain optimisation
What do you see happening in the AI and Big Data industry in manufacturing in the next 12-18 months?
Honestly, I think we’ll see a continuance of where we’ve already been going for the last 12- 18 months. AI and data are already being used in manufacturing but this use doesn’t get as much attention in the media as, say, healthcare, but the success stories are there, and they will continue as operations continue their digital journeys.