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Experts Weigh In: What Does It Take to Succeed in AI?

The world of AI technology is rapidly changing and evolving, and it can be overwhelming to know where to start. According to the 2022 ML Insider Report by, lack of knowledge and expertise, and hiring AI/data science talent remain the top 2 challenges executing ML programs. With the growing demand for AI experts, many people are wondering ‘What does it really take to succeed in AI?’.

To find out, asked experts and innovators in the industry what they believe is necessary to succeed in AI. Read on to discover their top tips, advice, and valuable insights on the keys to success in AI.

For the full list of AI developers to watch in 2023 go here

Massimo Belloni, Data Science Manager, Bumble inc. 

To be successful in AI it takes a good mix of patience and pragmatism: Patience, to be able to deal with the long and (possibly!) boring phases around dataset cleaning, feature collection and processes understanding; Pragmatism, in order to be able to deploy to production as soon as possible with the best and quickest stack available to prove value to the business right from the first steps.

Alessandra Sala, Sr. Director of AI and Data Science at Shutterstock 
Global President of Women in AI

Success in AI, for me, means building AI Systems that deliver value and equitable outcomes. AI powered systems require a multilateral approach: 1. deep technical knowledge to understand the fundamental limits and assumptions built in AI models; 2. strong AI engineering practices for production deployment which requires a different lifecycle to traditional software; 3. Ethical and Responsible AI practices to audit the AI system, its assumptions and the data used for training.

Jonathan Jin, Senior ML Engineer, Spotify

I think working in AI really benefits from a curious mindset and thirst for multi-disciplinary learning. One needs to understand not just how to build ever bigger and more powerful models but also remain cognizant of the ethical and sociological concerns around relying on AI systems—along with their hidden assumptions and human costs—as the interface for larger and larger swathes of how we interact with and comprehend the world. No system can be decoupled from the people that use it and are impacted by it. I believe this is particularly true of AI systems. And I look forward to collaborating with folks who understand that it’s not all simply about benchmarks or speed or models with ever-increasing numbers of parameters, but rather about the impacts of such systems to the humans that use them—both good and bad.

Petar Veličković, Staff Research Scientist, DeepMind

Success in AI requires, primarily, tenacity in my opinion. It’s truly a field with one of the easiest ‘barriers of entry’, and it’s never too late to join in on the action – so long as you are highly motivated to stay on top of the field. I speak from experience: when I started my PhD in machine learning, I had zero experience of modern deep learning methods. In fact, I spent the first few months of this PhD taking a Udacity course to learn TensorFlow! Not long after, I was making published research contributions in computer vision, and later, I got admitted to a visiting position at Montréal’s prestigious Mila institute, and published my top-cited paper on graph attention networks. A little perseverance, coupled with a decent grounding in linear algebra and probability, can truly go a long way.

Irene Solaiman, Policy Director, Hugging Face

Collaborating with experts across fields will better inform my holistic approaches to both AI engineering and to social impact work. My personal mission is to improve AI for peoples most often marginalized or overlooked. In addition to researching technical methods to mitigate societal harms from applied AI systems, such as harmful biases against protected classes, I shape proactive policies to standardize and steer AI development for public interest. This requires computer science skills and programming knowledge, deep familiarity with policy frameworks for AI, and robust understanding of the complex social ecosystem in which we develop and deploy AI systems. Interdisciplinary skills are key.

Leonardo Neves, Manager, Applied Research, Grammarly

Being successful in AI requires being curious and able to adapt. The algorithms and tools I used in graduate school are already outdated. I had to constantly read and learn new things throughout my career, and not only topics related to my core area of research — Natural Language Processing — but also several other areas like Computer Vision, Graph Neural Networks, and even Psychology. A good mix of breadth and depth is encouraged if you are looking for a career in AI. Many ideas transcend a specific area of expertise and can help you be creative as you solve your problems.

Murilo Gustineli, Data Scientist, Insight

To be successful in AI, it is important to have a strong foundation in mathematics, computer science, and statistics. Being curious and willing to learn is essential to stay relevant in the field. Having passion for learning, perseverance at a certain task, and creating purposeful activities will take someone to the top of their field. 

Companies should prioritize engineering. Implementing machine learning is first and foremost a software endeavour, and requires experience building well architected, reliable, easy to deploy software. Also, prioritize time spent on data quality over model tuning. More efforts should be spent on getting more relevant input data, and preprocessing the data in a better way. Choosing the right algorithm and tuning it correctly is the last step.

Ria Cheruvu, AI Ethics Lead Architect, Intel Corporation

2 things stand out as important for AI success. The enthusiasm to learn and keep up with AI, as a rapidly growing domain. And a question-driven perspective to investigate and challenge the technical details of AI implementations. I see that learning and implementing responsible data practices from the ground-up when building AI systems is a key part of this, to be able to recognize and refine AI models and the guardrails we place around them.

Celine Xu, Lead Data Scientist, H&M group

Successful AI initiatives depend on a number of things. AI should be used to focus on measures and optimize business value such as income, profitability, and engagement. Models should be created with the customer or user in mind in order to solve pain points rather than just doing fancy stuff. Change management is important to focus on when implementing AI solutions so that they are successfully integrated into business operations and processes, not just providing a product. The limitations of AI should be kept in mind so that machines are only used for tasks which they can complete more efficiently than humans. This will free up people to work on more strategic and innovative tasks.

Nermeen Louizi Ghoniem, AI Engineer, Jabra

A successful AI developer is eager to contribute and yearning to learn more but also understands the complexity of modern society and understands the value of collaboration – a true hybrid that can balance even the most complex situations and always has an eye for the bigger picture.

Laura Edell, Chief Data Scientist, Microsoft

To be successful in AI, it’s important to speak in terms of the business outcomes you are trying to achieve through using AI, rather than using PhD-level language that only a small group of people will understand.

Leanne Fitzpatrick, Director of Data Science, Financial Times

To be successful in AI I believe you need a healthy blend of creativity, problem-solving and value-driving focus. This blend enables the ability to apply algorithms and solutions to novel or nascent problems to deliver cutting edge solutions. I firmly believe that technical abilities (coding, mathematics and statistics) can be taught, whereas great instincts to problem solve and look at challenges in a different way are inherent. I also believe that we need to continue to encourage a diverse range of voices in the AI & ML space, as this will help not only create more inclusive solutions but also push innovation beyond the status quo.

Bayan Bruss, Senior Director, Machine Learning Engineering, Capital One

There are many factors that foster success in AI, but two key components come to mind as the most impactful and critical – engineering and science. No research survives its first contact with a real-world system. The key to impactful AI lies within the “how” each system is engineered. Oftentimes, the engineering challenges are completely independent of the research challenges. It’s best to take a no-handoffs approach where the people who are figuring out if something is possible are also making it a reality.

For the full list of AI developers to watch in 2023 go here

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