Advice for young scientists — and curious people in general

The Nobel Prize-winning biologist Peter Medawar published "Advice to a Young Scientist" in 1979. Here are some of Medawar’s key insights from the book.

Editor's note: This article, which is a selection of quotes from "Advice to a Young Scientist" coupled with commentary from Farnam Street staff, originally ran in May 2021 on the Farnam Street blog. It is reprinted here in its entirety with the gracious permission of Farnam Street.

The Nobel Prize-winning biologist Peter Medawar (1915–1987) is best known for work that made the first organ transplants and skin grafts possible. Medawar was also a lively, witty writer who penned numerous books on science and philosophy.

In 1979, he published Advice to a Young Scientist, a book brimming with both practical advice and philosophical guidance for anyone “engaged in exploratory activities.” Here, we summarize some of Medawar’s key insights from the book.

Application, diligence, a sense of purpose

“There is no certain way of telling in advance if the daydreams of a life dedicated to the pursuit of truth will carry a novice through the frustration of seeing experiments fail and of making the dismaying discovery that some of one’s favourite ideas are groundless.”

If you want to make progress in any area, you need to be willing to give up your best ideas from time to time. 

A black and white profile shot of the Nobel Prize-winning biologist Peter Medawar
The Nobel Prize-winning biologist Peter Medawar (1915–1987) is best known for work that made the first organ transplants and skin grafts possible.
By Digitised for CODEBREAKERS, MAKERS OF MODERN GENETICS

Science proceeds because researchers do all they can to disprove their hypotheses rather than prove them right. Medawar notes that he twice spent two whole years trying to corroborate groundless hypotheses. The key to being a good scientist is the capacity to take no for an answer— when necessary. Additionally:

“…one does not need to be terrifically brainy to be a good scientist…there is nothing in experimental science that calls for great feats of ratiocination or a preternatural gift for deductive reasoning. Common sense one cannot do without, and one would be the better for owning some of those old-fashioned virtues which have fallen into disrepute. I mean application, diligence, a sense of purpose, the power to concentrate, to persevere and not be cast down by adversity—by finding out after long and weary inquiry, for example, that a dearly loved hypothesis is in large measure mistaken.”

The truth is, any measure of risk-taking comes with the possibility of failure. Learning from failure to continue exploring the unknown is a broadly useful mindset.

How to make important discoveries

“It can be said with marked confidence that any scientist of any age who wants to make important discoveries must study important problems. Dull or piffling problems yield dull or piffling answers.”

A common piece of advice for people early on in their careers is to pursue what they find most interesting. Medawar disagrees, explaining that “almost any problem is interesting if it is studied in sufficient depth.” He advises scientists to look for important problems, meaning ones with answers that matter to humankind.

When choosing an area of research, Medawar cautions against mistaking a fashion (“some new histochemical procedure or technical gimmick”) for a movement (“such as molecular genetics or cellular immunology”). Movements lead somewhere; fashions generally don’t.

Getting started

Whenever we begin some new endeavor, it can be tempting to think we need to know everything there is to know about it before we even begin. Often, this becomes a form of procrastination. Only once we try something and our plans make contact with reality can we know what we need to know. Medawar believes it’s unnecessary for scientists to spend an enormous amount of time learning techniques and supporting disciplines before beginning research:

“As there is no knowing in advance where a research enterprise may lead and what kind of skills it will require as it unfolds, this process of ‘equipping oneself’ has no predeterminable limits and is bad psychological policy….The great incentive to learning a new skill or supporting discipline is needing to use it.”

The best way to learn what we need to know is by getting started, then picking up new knowledge as it proves itself necessary. When there’s an urgent need, we learn faster and avoid unnecessary learning. The same can be true for too much reading:

“Too much book learning may crab and confine the imagination, and endless poring over the research of others is sometimes psychologically a research substitute, much as reading romantic fiction may be a substitute for real-life romance….The beginner must read, but intently and choosily and not too much.”

We don’t talk about this much at Farnam Street, but it is entirely possible to read too much. Reading becomes counterproductive when it serves as a substitute for doing the real thing, if that’s what someone is reading for. Medawar explains that it is “psychologically most important to get results, even if they are not original.” It’s important to build confidence by doing something concrete and seeing a visible manifestation of our labors. For Medawar, the best scientists begin with the understanding that they can never know anything and, besides, learning needs to be a lifelong process.

The secrets to effective collaboration

“Scientific collaboration is not at all like cooks elbowing each other from the pot of broth; nor is it like artists working on the same canvas, or engineers working out how to start a tunnel simultaneously from both sides of a mountain in such a way that the contractors do not miss each other in the middle and emerge independently at opposite ends.”

Instead, scientific collaboration is about researchers creating the right environment to develop and expand upon each other’s ideas. A good collaboration is greater than the sum of its parts and results in work that isn’t attributable to a single person.

For scientists who find their collaborators infuriating from time to time, Medawar advises being self-aware. We all have faults, and we too are probably almost intolerable to work with sometimes.

When collaboration becomes contentious, Medawar maintains that we should give away our best ideas.

Scientists sometimes face conflict over the matter of credit. If several researchers are working on the same problem, whichever one finds the solution (or a solution) first gets the credit, no matter how close the others were. This is a problem most creative fields don’t face: “The twenty years Wagner spent on composing the first three operas of The Ring were not clouded by the fear that someone else might nip ahead of him with Götterdämmerung.” Once a scientific idea becomes established, it becomes public property. So the only chance of ownership a researcher has comes by being the first.

However, Medawar advocates for being open about ideas and doing away with secrecy because “anyone who shuts his door keeps out more than he lets out.” He goes on to write, “The agreed house rule of the little group of close colleagues I have always worked with has always been ‘Tell everyone everything you know,’ and I don’t know anyone who came to any harm by falling in with it.

How to handle moral dilemmas

A scientist will normally have contractual obligations to his employer and has always a special and unconditionally binding obligation to the truth.

Medawar writes that many scientists, at some point in their career, find themselves grappling with the conflict between a contractual obligation and their own conscience. However, the “time to grapple is before a moral dilemma arises.” If we think an enterprise might lead somewhere damaging, we shouldn’t start on it in the first place.

We should know our values and aim to do work in accordance with them.

The first rule is never to fool yourself

“I cannot give any scientist of any age better advice than this: the intensity of the conviction that a hypothesis is true has no bearing of whether it is true or not.”

Richard Feynman famously said, “The first principle is that you must not fool yourself—and you are the easiest person to fool.” All scientists make mistakes sometimes. Medawar advises, when this happens, to issue a swift correction. To do so is far more respectable and beneficial for the field than trying to cover it up. Echoing the previous advice to always be willing to take no for an answer, Medawar warns about falling in love with a hypothesis and believing it is true without evidence.

“A scientist who habitually deceives himself is well on the way toward deceiving others.”

The best creative environment

“To be creative, scientists need libraries and laboratories and the company of other scientists; certainly a quiet and untroubled life is a help. A scientist’s work is in no way deepened or made more cogent by privation, anxiety, distress, or emotional harassment. To be sure, the private lives of scientists may be strangely and comically mixed up, but not in ways that have any special bearing on the nature and quality of their work.”

Creativity rises from tranquility, not from disarray. Creativity is supported by a safe environment, one in which you can share and question openly and be heard with compassion and a desire to understand.

A final piece of advice

“A scientist who wishes to keep his friends and not add to the number of his enemies must not be forever scoffing and criticizing and so earn a reputation for habitual disbelief; but he owes it to his profession not to acquiesce in or appear to condone folly, superstition, or demonstrably unsound belief. The recognition and castigation of folly will not win him friends, but it may gain him some respect.”

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US, WA, Bellevue
At AWS, we use Artificial Intelligence to be able to identify every need of a customer across all AWS services before they have to tell us about it and help customers adopt best practices while architecting on the cloud. We are looking for Applied Scientists to drive innovation with Gen AI to bring paradigm shift to how the business operates and build “best in the world” experience that customers will love! Some of the science challenges we work on include fine-tuning Large language models for domain specific use cases, Reinforcement Learning, Auto-generating code from natural language and generating strategic insights and recommendations from very large datasets. You will have an opportunity to lead, invent, and design tech that will directly impact every customer across all AWS services. We are building industry-leading technology that cuts across a wide range of ML techniques from Natural Language Processing to Deep Learning and Generative Artificial Intelligence. You will be a key driver in taking something from an idea to an experiment to a prototype and finally to a live production system. Our team packs a punch with principal level product, science, engineering, and leadership talent. We are a results focused team and you have the opportunity to lead and establish a culture for the big things to come. We combine the culture of a startup, the innovation and creativity of a R&D Lab, the work-life balance of a mature organization, and technical challenges at the scale of AWS. We offer a playground of opportunities for builders to build, have fun, and make history! AWS Utility Computing (UC) provides product innovations — from foundational services such as Amazon’s Simple Storage Service (S3) and Amazon Elastic Compute Cloud (EC2), to consistently released new product innovations that continue to set AWS’s services and features apart in the industry. As a member of the UC organization, you’ll support the development and management of Compute, Database, Storage, Internet of Things (Iot), Platform, and Productivity Apps services in AWS, including support for customers who require specialized security solutions for their cloud services. Key job responsibilities - Deliver real world production systems at AWS scale. - Work closely with the business to understand the problem space, identify the opportunities and formulate the problems. - Use machine learning, data mining, statistical techniques, Generative AI and others to create actionable, meaningful, and scalable solutions for the business problems. - Analyze and extract relevant information from large amounts of data and derive useful insights. - Work with software engineering teams to deliver production systems with your ML models - Establish scalable, efficient, automated processes for large scale data analyses, model development, model validation and model implementation A day in the life Diverse Experiences AWS values diverse experiences. Even if you do not meet all of the qualifications and skills listed in the job description, we encourage candidates to apply. If your career is just starting, hasn’t followed a traditional path, or includes alternative experiences, don’t let it stop you from applying. Why AWS? Amazon Web Services (AWS) is the world’s most comprehensive and broadly adopted cloud platform. We pioneered cloud computing and never stopped innovating — that’s why customers from the most successful startups to Global 500 companies trust our robust suite of products and services to power their businesses. Inclusive Team Culture Here at AWS, it’s in our nature to learn and be curious. Our employee-led affinity groups foster a culture of inclusion that empower us to be proud of our differences. Ongoing events and learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences, inspire us to never stop embracing our uniqueness. Mentorship & Career Growth We’re continuously raising our performance bar as we strive to become Earth’s Best Employer. That’s why you’ll find endless knowledge-sharing, mentorship and other career-advancing resources here to help you develop into a better-rounded professional. Work/Life Balance We value work-life harmony. Achieving success at work should never come at the expense of sacrifices at home, which is why we strive for flexibility as part of our working culture. When we feel supported in the workplace and at home, there’s nothing we can’t achieve in the cloud. Hybrid Work We value innovation and recognize this sometimes requires uninterrupted time to focus on a build. We also value in-person collaboration and time spent face-to-face. Our team affords employees options to work in the office every day or in a flexible, hybrid work model near one of our U.S. Amazon offices.
US, CA, Sunnyvale
The Amazon Artificial General Intelligence (AGI) Personalization team is looking for a passionate, highly skilled and inventive Applied Scientist with strong machine learning background to build state-of-the-art ML systems for personalizing large-scale, high-quality conversational assistant systems. As a Applied Scientist, you will play a critical role in driving the development of personalization techniques enabling conversational systems, in particular those based on large language models, information retrieval, recommender systems and knowledge graph, to be tailored to customer needs. You will handle Amazon-scale use cases with significant impact on our customers' experiences. Key job responsibilities - Use deep learning, ML and NLP techniques to create scalable solutions for creation and development of language model centric solutions for building personalized assistant systems based on a rich set of structured and unstructured contextual signals - Innovate new methods for contextual knowledge extraction and information retrieval, using language models in combination with other learning techniques, that allows effective grounding in context providers when considering memory, compute, latency and quality - Research in advanced customer understanding and behavior modeling techniques - Collaborate with cross-functional teams of scientists, engineers, and product managers to identify and solve complex problems in personal knowledge aggregation, processing, modeling, and verification - Design and execute experiments to evaluate the performance of state-of-the-art algorithms and models, and iterate quickly to improve results - Think Big on conversational assistant system personalization over a multi-year horizon, and identify new opportunities to apply these technologies to solve real-world problems - Communicate results and insights to both technical and non-technical audiences, including through presentations and written reports About the team The AGI Personalization org uses various contextual signals to personalize Large Language Model output for our customers while maintaining privacy and security of customer data. We work across multiple Amazon products, including Alexa, to enhance the user experience by bringing more personal context and relevance to customer interactions.