Diversity In AI Series: Samsung’s Yue Wang On Propelling The AI Industry Forward4th February, 2022
For my final deep dive into how we can create a more diverse and resultantly, stronger AI and Machine Learning workforce, I am thrilled to be joined by an individual working at a tech powerhouse: Yue Wang. Yue Wang is someone truly working at the forefront of AI and 5G research and development at Samsung and has a huge amount of insight to offer about the future of AI and the workforce and how she got to where she is today.
I talk to companies every day in my role as head of AI and Machine Learning recruitment about their struggle to fill roles. It’s no secret that talent is in short supply for jobs in machine learning, and many other tech sectors, so what’s the solution to attracting new individuals and retaining the talent you do have at your company?
I believe it’s integral to any company’s success to have a clear commitment to Diversity and Inclusion, and not just one on paper, but one evident the moment you walk through a company’s door. It’s not just a ‘nice to have,’ but a necessity for any hiring strategy in 2022 beyond to be transparent about gender pay, have things in place to tackle unconscious bias, strive for a multigenerational workforce, acknowledge a variety of religious and cultural holidays, for example.
Yue’s path to Samsung
With this in mind, I want to reflect on diversity in technology and the overall impact on the industry, with my special guest, Yue Wang, Senior Technology Manager and network AI lead at Samsung UK. Yue is working on using AI for optimisation and management for 5G and beyond networks a new and exciting arena for Samsung, that is only set to grow at a rapid pace.
“This path kind of came naturally,” says Yue. “I have always been interested in technology and engineering. I started my career as a PhD working on the physical layer of the network, and then I gradually broadened the area to the higher layers and network.”
Yue’s work in network AI research and development is focused on understanding and to some extent influencing the industry training or generating ideas, concept use cases, papers, patents and their contributions, which are later tested.
“Our development is where we need to put our ideas into reality from processing, getting and processing the right data to designing and integrating AI models into the realistic scenarios,” explains Yue. “Then we spend a long time on training models, so we can then actually validate and demonstrate it. Following this we enhance the product – for instance, cooperate with our headquarter and also work on third party products with external partners (e.g. Universities). So, overwrite your work and never get bored!”
How did Yue get to this point and joining a company with a household tech name like Samsung?
“I come from China, and the STEM subjects were very popular at the time I was there, and still continue to be very popular in China,” states Yue. “It wasn’t a hard choice at all. I was encouraged by my parents and friends of my parents, who shared lots of good things about working in the industry.”
“This path kind of came naturally,” says Yue. “I have always been interested in technology and engineering. I started my career as a PhD working on the physical layer of the network (i.e. the lowest layer of the network protocol), and then I gradually broadened the area to the higher layers and network.”
“What I like about working in this industry and what has motivated me to stay for so long/until now, is that there are always new things to learn and explore,” she comments. “Like starting out with the physical layer and gradually broadened my areas to the higher layer and the core network in AI.” Yue affirms it’s also a key motivator that she always has the opportunity to take part in shaping new technologies and make an impact.
What’s the value of AI networks?
Making an impact is certainly something Yue is achieving with the application of AI to network operation management she’s involved with, however, it has been a three to four-year journey to get to the point of what she was fascinated by becoming more of a reality.
“There is lots of activity around AI networks at the moment, but when we first tried to put AI in the network, we’d get a lot of questions like, ‘why do you want to use AI?’ and ‘what’s the benefit?’
The bottom-line question is always, ‘why would anyone invest in that?’, so I had to put in a lot of groundwork.”
“I’m convinced this is the way to go, as you can simplify the network operation and save a great deal of network operational cost.”
Yue looked into it more and gradually started to build her skill set and crucially, put together a team to work on some of the “really interesting” work when it comes to understanding the concept she had in mind, and actually being able to bring it to life in a practical life simulation.
“To finally build a team and secure funding working on something really interesting has been a really rewarding and a fascinating journey. For me, it’s like planting a seed and gradually seeing it grow into a tree, it’s that sort of satisfaction.”
Yue has high hopes what she’s working on now will be an integral part of network operation management in the future; “I see AI will become inherent in the network, everywhere in different network domains and for different purpose of the network operations.”
“Making an impact like this is something I would never have thought I’d be able to do when I first joined the industry.”
The importance of considering cultural diversity
Oftentimes the conversation regarding diversity in the workplace can centre around diversity in the workplace, which is obviously a hugely important conversation, but there has to be room to talk about creating an open space for cultural diversity too.
“Things can happen quite often in workplaces where people who come from a different cultural background, that even though they are well educated, they can still sometimes not have enough of the cultural context in order to understand some of the conversations,” explains Yue. She highlights the example of a cartoon reference used in training only staff who grew up in Western culture might get, and taking this into account.
“I think this can sometimes make people shy away from discussions and discourage them from contributing their ideas.” This is something many may overlook when it comes to references used in conversations and training, and Yue advises just that extra effort is needed to give context to support understanding of those who haven’t grown up with the same references.
“Things can happen quite often in workplaces where people who come from a different cultural background... they can sometimes not have enough of the cultural context in order to understand some of the conversations”
Gender needs to be part of the conversation too, as we’ve discussed a lot in previous Diversity in AI interviews, there is a lot of work to be done to make the workplace a level playing field and reduce unconscious bias. “Studies show that 70% of men are less willing to accept a criticism from a woman, and more likely to dismiss it,” says Yue. “Think about how this would impact a woman in their everyday work – we need more awareness. For example, next time we hear someone expressing a different opinion we don’t like, take time to reflect on why this may be.”
“Whether you’re a man or woman, British or Chinese, you will always be facing some sort of challenge in your day-to-day work,” states Yue. “Although the challenges are different, it’s important to look at the culture in the team or in the company, and try to think about things from a different perspective – for instance, another’s gender’s perspective, another coach’s perspective and another individual’s perspective overall when it comes to reaching an agreement or negotiating a project.” She summarises, “I think that goes a long way, regardless of your background.”
Advice for those applying for AI & Machine Learning jobs
Working in the AI & Machine Learning industry, I’m all too aware of the gender diversity gap – as shown in our recently published 2022 Talent & Salary Report. On average, there are three men for every female in the industry, and things have to change. What’s Yue’s advice as someone creating waves in the sector in a leadership role?
“It’s important to know outnumbering doesn’t mean you’re not welcome [as a woman], although It is a male-dominated industry,” says Yue. “In my experience, in every workplace the majority of people are professional, supportive, and want to make you feel welcome.”
“My other advice is to be yourself – just because you’re the minority and you want to fit in, it doesn’t mean you need to change who you are (e.g. be an alpha woman or be super nice). Over time, with confidence, you might change the way you do things, but this should come naturally.
“In the professional world, you get respect with your expertise, skills, knowledge and how you treat others, regardless of your gender.”
As a recruiter involved in the industry for several years, I am seeing more women come through the ranks from different backgrounds and it feels like an exciting time and a time for change when it comes to diversity.
Being involved with hiring, Yue also sees first-hand how candidates sell themselves and has advice for minorities in AI going for a role they may be very passionate about.
“In the past hiring for roles like a Network AI Researcher, I have found women, as an example, would show less competence when pitching themselves for a role (e.g. talk small about themselves,” explains Yue.
Yue says it’s imperative to talk more about what they have shown they can do previously; “I want candidates to talk more about their positive achievements instead of being too modest!”
A big thank you to Yue Wang for chatting with me for my Diversity in AI series and sharing more about exciting developments being made in network AI. Ready for a change of work scenery? The AI & Machine Learning job market is extremely healthy, and we would love to find out more about what you’re looking for.