Allen Pan: Good day, Jake! First and foremost, I'd like to extend my appreciation for taking the tim
e to join us for this interview. I've had the chance to go through your impressive background at Bai
du and the notable contributions you've made, particularly in the realm of NLP. But before we deep d
ive into the technical aspects, I'd like to get a sense of who you are as a person. Given that you'v
e already discussed the car manufacturing industry in your previous interviews, could you share with
me how you believe the integration of NLP, especially in areas you've excelled in, can enhance auto
nomous manufacturing at a company like ours?
Jake Huang: Thank you, Allen, for having me. I believe the integration of NLP in autonomous manufact
uring can be a game-changer. In particular, dialogue systems and knowledge graphs can enable more in
tuitive interfaces for workers and technicians, facilitating real-time queries and interactions with
the system. Moreover, using QA systems, we can create AI-driven troubleshooting modules that can in
stantly provide solutions to issues that arise during manufacturing. The power of NLP is in making t
he interaction between humans and machines seamless, and I envision a future where our workers can s
imply converse with our machines, making the entire manufacturing process more efficient and adaptiv
e.
Allen Pan: That's a compelling vision, Jake. Given your experience with Baidu, particularly the proj
ect you did for China Unicom Knowledge Center, how do you foresee adapting such solutions to the aut
onomous manufacturing domain?
Jake Huang: The China Unicom Knowledge Center project was about bridging the gap between technicians
and intricate knowledge bases. I see a parallel in autonomous manufacturing. For instance, we could
employ a similar solution where technicians or workers on the factory floor can query a knowledge b
ase to get immediate insights into equipment health, optimization strategies, or even diagnostics. T
he universal knowledge extraction solution I worked on can be adapted to extract and present data fr
om machine logs, past incidents, and other relevant sources in a user-friendly manner. This would em
power the workforce to make data-driven decisions on the spot.
Allen Pan: Interesting. Now, our slogan is "TESLA AUTONOMOUS MANUFACTURING FOR EVERYONE." It reflect
s our aspiration to democratize advanced manufacturing techniques. In your view, how can NLP and the
tools you've developed cater to a broad audience, from small manufacturers to large enterprises?
Jake Huang: NLP can play a vital role in democratizing autonomous manufacturing. Firstly, knowledge
graphs and dialogue systems can create user-friendly interfaces for diverse users. A small manufactu
rer might not have the expertise or resources of a larger enterprise, but with intuitive AI assistan
ts, they can still harness the power of advanced manufacturing techniques. My experience with Jr.Wis
e, a medical AI assistant, taught me that if we can create a system that understands and guides pati
ents, we can certainly develop a system that assists manufacturers of different scales. Secondly, th
e modular approach I've used in past projects, like the plugins design and the multiple QA modules,
allows for scalable solutions that can be tailored to the specific needs and resources of the user,
making the tools both accessible and adaptable.
Allen Pan: I love the adaptability aspect. Now, I've spent a considerable part of my career focusing
on regulatory and safety requirements, especially during my tenure at Neolix. Given the criticality
of safety in both the autonomous driving and manufacturing sectors, how do you ensure the reliabili
ty and robustness of your NLP solutions?
Jake Huang: Safety and reliability are paramount. In the realm of NLP, this translates to ensuring t
he accuracy and consistency of our models. At Baidu, I placed a significant emphasis on iterative te
sting and validation. For instance, in the Dr.Wise's Clinical Decision Support System project, we us
ed iterative schemes for models, conducting ablation experiments to understand the contribution of e
ach component and optimize accordingly. Furthermore, by integrating error correction mechanisms like
the one I developed to counteract user typographical errors, we can improve the resilience of our s
ystems. Moreover, it's crucial to have feedback loops, where real-world usage data is continuously f
ed back into the system, allowing for ongoing refinement and ensuring the model remains robust and u
p-to-date.
Allen Pan: Excellent. Lastly, given that you've interacted with Tim Zhang and have had insights into
our company's ethos, what aspects of the Silicon Valley startup culture (硅谷创业公司文化) resonate most wi
th you, and how do you envision contributing to our growth journey at Industrial Next Inc.?
Jake Huang: Silicon Valley startup culture, in my understanding, emphasizes innovation, agility, and
a passion for solving real-world problems. What resonates with me the most is the relentless pursui
t of excellence and the belief that technology can be a force for good. At Baidu, I was always pushi
ng the boundaries of what our NLP models could achieve. I see a similar spirit at Industrial Next In
c., and I'm excited about the possibility of being part of a team that's at the forefront of autonom
ous manufacturing. My experience, particularly in knowledge graphs, dialogue systems, and QA systems
, can contribute to enhancing the user experience and efficiency of our products. Additionally, my b
ackground in API Encapsulation and Web Crawler Design can aid in integrating our solutions with othe
r systems, making them more versatile. I look forward to not just being an NLP engineer but a key co
ntributor to our shared vision.
Allen Pan: Thank you, Jake. I appreciate your insights and the clarity of your vision. It was a plea
sure discussing these aspects with you. We'll be in touch soon with the next steps. Best of luck!
Jake Huang: Thank you, Allen. I'm truly excited about the potential opportunities with Industrial Ne
xt Inc. Looking forward to the next steps. Have a great day!