I’m writing this in response to a number of people who approached me with queries regarding Adobe India Women in Tech. Scholarship. I will be covering the entire process and possible queries an applicant might have. I will be presenting my perspective of making a good application. However, I won’t be able to point out the specific factors which led me through it, as I am unaware of that. I hope this helps all the potential WIT scholars.

Preliminaries

Eligibility

You should be a pre-final year bachelors or integrated-masters female-identifying student doing a major or a minor in CS or a related field — please also confirm this from the official website.

Benefits

• Monetary reward, accommodating the cost of remaining academic year.
• A research internship opportunity at Adobe Research, India — usually based on NLP, CV, Analytics, etc.

As I see myself working in ML research in the future, the scholarship along with the internship turned out to be an excellent opportunity and exposure for the same. I believe this experience will be an important milestone in my career path ahead. If you are an undergrad envisioning yourself as an ML researcher, this is one best opportunities out there. With this, I think there is enough motivation to make the effort of sending in an application.

The Process

The Application

The application asks for some general information, your areas of interest, and a resume — a decent GPA and 3–4 good tech projects might make a good resume — your profile must project that you’re enthusiastic about the tech you are pursuing, and that’s all about your factual history. The long answer questions are more involved — they are about your future plans and the motivation behind it, the difficulties you faced as a woman in tech, an event that changed your life, what role does this specific scholarship plays in your overall story, and why you should be considered, etc. Answering these questions without introspection won’t make any sense. Take some time to think about the story of your life, try to tie the elements from the past to your present actions and efforts, and have an aligned vision of the future. Try not to be cliché — for the application and for yourself too.

The objective of the scholarship is to help you fulfill the goals you have in any tech field. You should have a plan which is facilitated if you get the scholarship. So try not to target it, instead target your actual goals. The past and present of your story should reflect the commitment towards the tech you are passionate about, through your involvements/achievements. You also need to justify your motivation for the same, which can be your personal interests in pursuing whatever you want to. Once the motivation is clear and strong, the future plan can be a direct entailment — finding out the ways you can explore your interests at best. An event that changed your life could also be a set of events, or a period in your life. I didn’t have a specific event, but yes some experiences put together did change my perspective.

I will refrain myself from biasing your stories anymore, and let you introspect. I hope the above prompts help you figure out your answers for the application questions. I will also suggest thinking about your own original answer, since my suggestions are not unique anymore. The actual questions can be slightly or entirely different, but I hope you get the drift. The next and the final component is an LOR — try to get it from a professor who knows you well and appreciates your abilities — so that it is strong enough. With this, we shall move to the next step.

The Interview(s)

Once your application is shortlisted, you are invited for online interviews. Since you spent time thinking about your story and you have locked your application (by the end of September), believe in your story from now on and try to be consistent with it during the interviews. Most of my interview questions were a sub/super-set of application questions. Be ready to explain any part of your resume. You may get questions like what’s the best project you did according to you, and then follow-ups can go in any direction — depending on what you emphasize. Keep everything on the top of your head — like what’s your contribution in a particular project, why was it an important idea, etc. It’s important to be thorough with the resume at technical and philosophical levels. I had my interviews during November, and the results were announced towards the end of December.

Take Aways

The following are the key aspects (according to me) which might get you through the scholarship:

1. Your resume must show your enthusiasm and competence towards the tech field you work in — via projects, competitions, etc.
2. Try to get your LOR as strong as possible, as this is the only thing you’re not writing for yourself, and hence is highly credible.
3. Your story will shine out if it’s genuine and sound yet unique — and that takes some serious self-analysis to put together the experiences you got.
4. There can be questions you don’t have an answer to, but try to fit the closest answer or change the question a little according to you.

At last, I’ll emphasize again on the fact that this scholarship is purposed for encouraging and helping an individual with her long-term goals. I suggest that you focus on the same and while doing so, if you really deserve it, it will fall along your way.

Machine communication has seen much excitement in the past few decades. Artificial Intelligence(AI), specifically, has seen much advancement in the recent decades. Central to artificial intelligence is an artificial personality. The term ‘artificial’ is a relational concept; it suggests an imitation of the ‘natural’. AI design involves creating a human copy and replicating human activities for achieving performance benefits. The replication is the most challenging step in AI design.

AIs and their personality

While canvassing personality, it becomes necessary to take a holistic view of personality. Rather than describing it in terms of its components which often misleads while we claim that AI has personality, we can describe it in terms of its various dimensions. Specifically, personality has four dimensions :physical, emotional, mental, and social and moral personality.

Physical Personality: This covers those domains which can be judged by people in the first meeting; it is equivalent to a ‘first impression’. How one looks, how smart and intelligent they are, how open and agreeable, and other such attributes are established during the initial contact with a subject. Thus, the first notions we form about individual subjects, say by characterizing them on the basis of how aggressive or competent they seem, or their sexual orientation, are directly related to the subject’s physical personality. While this dimension of personality seems achievable by AI bots, external knowledge often problematizes this achievement. Physical personality is affected by factors that determine the first impression. For example, suppose a robot impresses a person and the person consequently develops feelings for the bot. The justification of the person’s response cannot be separated from the justification and lawfulness of a machine-human relationship. Moreover, does the machine forms this first impression about people too? While we claim that it can be executed, I would like to involve a little biology behind this phenomenon and claim that we tend to like or dislike or form sexual orientation towards someone after the first impression as a result of the actions of a set of hormones. Now suppose the machine qualifies this test, then it is going to create a biological violation of laws if a machine wishes to involve with a human romantically.

Emotional Personality: Briefly, it describes a person in terms of their responses to several emotional situations. Human incorporates an emotion or a mix of it behind each and every exercise it performs, whether it’s eating, bathing, sleeping, waking up in the morning, or performing a hobby, or going to school, entering a silent zone or meeting a new person, etc. There are times when human faces emotional breakdowns, they need company and at times they provide company. There are many rigorous efforts ongoing in the field of AI to accommodate all these minute intricacies of emotions, one of the models being the PAD Emotion Model, which expands differently as a function of pleasure, arousal, and dominance in different situations. But until we don’t incorporate the complete emotional personality model, one can claim that AI bots are deficit in terms of emotional personality.

Mental Personality: Here, we characterize a person on basis of one’s intelligence quotient, creativity, verbal and writing skills, etc. One can argue that technology has almost saturated AIs in this respect, the other can’t say that it’s a wrong argument. We developed from calculators, then a computer playing chess and cards, to solving problems like Eureka. This was the vertical where we started with computers, and today it’s the most developed part of personality in robots. But there we still lack on some grounds like artistic skills. I realized the same when I encountered a site, BotPoet, which provides a free play to classify whether a given poem is written by a bot or a human. This is based on an idea proposed by Alan Turing which aims to design a machine which acts such that an interrogator cannot distinguish it from human based on its responses. And while I looked at the statistics, I found that roughly around 70% people are able to classify the poems correctly. This helps us claim that mental personality, though developed a lot, still has a gap and still is partial.

Social and Moral Personality: It describes how a person involves and behaves in a social setting. When we design a bot to solve a particular problem, let’s say for designing a dam on a river. There it might not think how it might affect residents of that place unless it is programmed for that. Inculcating values has a lot to do with the history of an individual, where again we can claim that a bot can be trained for the same which is correct in a sense. A bot which is trained for army purposes can be trained with social values but at times it might confuse itself with whether to kill a person or to save them. It might fail in its programming and that might turn harmful for human if they couldn’t figure out what command can control its behavior. The entire idea behind why a bot does not qualify social personality norms is, since we know there are imperfections in everything we make and can’t provide a full guarantee for anything, it might fail with its operations and might create harm in a way that even human cannot control.

Conclusion

AIs have reached far in developing an artificial personality but as the essay suggests there’s still a gap maintained between what they have achieved and what all is required to form a personality. I will agree that AI bots are converging very fast to match human and to get a personality, but as I mentioned, they are converging and a convergence never exactly approaches the actual thing. I believe that even after ten years from now, we’ll still be able to claim that a robot can’t have a personality as it can be altered entirely by human when required, i.e., their personality can be created/destroyed/changed anytime, considering that we expect some level of constancy when we think of a personality. We’ll still be able to claim about faithfulness, trust and safety issues as well as ethical issues. Binding it up, one can call what AIs have is a partial personality, but surely not a personality.

Is scientific discovery method-based/rational, or an outcome of human’s creative intuitions?

This article focuses on the arguments placed in the two texts, by Thomas Nickles¹ and Herbert A. Simon², on scientific discovery and whether the process of it is a rational, logical and method based or it is solely an outcome of creative intuition and human mind’s unbeatable efficiency in figuring out patterns. The paper by Simon brings in the distinction of inductivism from the object of discussion at hand, i.e., assuming either of the two positions will not fall into the problem of induction. This is logically sound because we are only trying to talk about if there exists an algorithm by which we can code up or normatively define what scientists and researchers do, without even touching upon if what and how they do it is correct or not, i.e., being free of the problem of induction. Having isolated our focus of discussion, we will move on to assessment of the two papers. An important element of problem definition, well explained in both the texts, is the hierarchical structure of scientific discovery, starting from identifying a plausible hypothesis, and then testing it to make verifiable predictions, i.e., justification process. More post-levels to this were added by Nickles in the later half of his text, backed up by examples about Einstein et al rediscovering later into the discovery made by Planck, thus underscoring his argument that a discovery process is never complete, and keeps redefining and rather keeps discovering into itself.

(Although the analysis is self-contained, but the reader is advised to refer to the texts this analysis is based on, to better appreciate the arguments)

Assessment

Simon’s effort to logically present the logic of method starts from defining a set of processes/hypothesis P which is constrained by a set of conditions — norms of testing laws C, and G is the goal of discovering valid scientific laws. Here, Simon assumes that the sets P and C are predefined and exhaustive, while scientists during actually heading towards a research might initially have a set of hypothesis (P), and ways to test them ©, but eventually may redefine the set as per subsequent explorations and results during the journey. Thus, if one wishes to program a discovery process, they may not be able to collect complete information to define those sets required to start the process. Further, the example of chess or tic-tac-toe game is claimed to follow a normative set of rules to achieve the Goal (which is to win the game). That is, the set is P, if not C, is tried to be achieved using an inductive (empirical) tool, which has problems too well known to be discussed. Hence, failing on the possibility to define such sets with confidence.

The paper further talks about the method and efficiency of discovering patterns in data, and describes a Heuristic Search Algorithm (HSA) which studies and draws information from the data, and then proposes a hypothesis which most optimally explains the pattern. The text tries to ground this formulation by citing an example from the history of scientific discovery — the Periodic Law of Mendeleev. It says that the discovery was an outcome of a pattern matching exercise after arranging the elements on the basis of atomic weights, which was known to Mendeleev, and probably an efficient computer program could work it out, given all the information. It also talks about that since the number of concepts are a finite set, but an infinite number of combinations of these exist, and a logic of discovery merely boils down to how to efficiently apply these combinations to make a discovery algorithmically. Since a set of finite basis is predefined thus, the author argues, that the PROCESS of making a discovery is deductive, while the discovery itself might not be (which is a common problem for all sources of discovery — human or machine). So the author claims that every discovery or so to say, novelty can be deduced. Thus, there’s no place of irrationality or original, unseen and entirely independent ideas to take any place. This implies that if we keep updating our set as the new discoveries are being made, then given an efficient algorithm and computational power, a pattern matching code could have done what Mendeleev did. In that case, let’s take a reverse walk in this process, dropping elements one by one from the finite set, and reach the nullity. Now here, how did it all start, how was the first discovery made, and how would the algorithm work with an empty set? Thus, the method identified to automate the discovery process seems to fail on doing a backwards recursion.

On the pattern matching examples quoted and the efficient algorithms discussed for those, since these cases were those when we knew the results, hence one could design an efficient algorithm. How do we make an algorithm efficient, i.e., efficiently picking up hypothesis and testing it on data, when the goal is not clear, i.e., an unknown phenomenon is being researched on. The paper by Nickles tries to solve this problem by proposing a context based algorithm which is more specific in the domain of research, so that it explores on only the relevant hypothesis, using only the relevant information. Even in this scenario there are flaws, let’s discuss the example of finding patterns in a given sequence of characters. Consider the argument that each human will not be equally efficient in finding that pattern, looks agreeable. Thus, some humans are more efficient than others. Then, we also need to agree that some humans couldn’t efficiently solve it even using a computer. One could make a computational process as efficient as they themselves are at max, because they can only use the algorithm they used to solve the problem using their mental faculties. Hence, the task of efficiently solving a problem depends on who is trying to solve it, themselves or computationally. Thus, there’s always a superiority of the human over the computer. How do we then explain these differences in efficiency of humans, and the limitations (human being the limit) of computers having efficient processes coded up to solve the problems? The process of making discoveries EFFICIENTLY is itself bringing in some non-excludable irrationality component, or something which is beyond epistemological scope going into the psychological domains.

The text by Nickles very rationally presents the entire evolution of debates on scientific discovery being rational or not. The author uses the word rational rather than logic, for the sake of greater irrefutability. The text introduces consequentialism based H-D method, i.e., discoveries should confirm consequences. It also identifies that discoveries are indeed temporally structured experimental processes, which do follow a method. But it expands the definition of discoveries so much, even beyond ‘final justification’, so that any algorithmic approach to formulate the process of discovery will not be able to match it exactly. It quotes that even the expert scientists will find it difficult to write down the method they will be applying for their research, and sometimes they might not follow or in fact violate the initial set of rules (the finite set that Simon talks about). Moreover, it uses a recursive argument against the existence of any logic of discovery, by quoting that if there were such logics, then how these logics themselves get discovered. It talks about the closest approaches, of putting the discovery process into an algorithmic framework, that has been formulated till now like genetic algorithms, which are great at learning BV+SR strategies, and are very powerful. Although the author seems to conclude that these logics could be efficient only if they are domain specific, and if we were to make a universal set of such logics, then it is equivalent to making scientific discoveries just inflexible.

Conclusion

I mostly agree with the arguments by Nickles, which very nicely explains the importance of studying logic of discovery processes, and the limitations that they face. In my opinion if one claims that there actually exists a perfect logic of discovery processes, then it implies that the process can equivalently be performed on a computer. The entire AI research is working on it, to make processes automatic as much as possible. The aim of AI is not to attain complete automation, but to augment human wherever the process can be feasibly formulated and can be performed efficiently by a computer, thus restating the importance of studying scientific discovery method. If we say that scientific discoveries are absolutely logical (methodological), with no element of irrationality or intuitions or novelty of thoughts involved, then it implies that someday AI could perfectly match and hence replace humans from all places. This is logically not possible, it might converge to it with an error but not match exactly. Apart from that, the ethical issues with AI replacing human also take considerable place in this debate. Can AI have a personality? is a short analysis on whether an artificial intelligence can ever converge to an actual personality or human intelligence, on the grounds of physical, mental, emotional, social and moral dimensions of personality. These dimensions of AI are worth studying since a discovery as in invention or a social construction is not merely a product of intelligent combination of known theories and facts, but are very intricately linked to various social and moral aspects, which are again as a whole making and giving scientists the power to innovate.

[1] Chapter 14: Discovery by THOMAS NICKLES (Pg. 85) of “A Companion to the Philosophy of Science” edited by W. H. NEWTON-SMITH, Blackwell Companions to Philosophy

[2] Simon, Herbert A. “Does Scientific Discovery Have a Logic?” Philosophy of Science, vol. 40, no. 4, 1973, pp. 471–480. JSTOR, www.jstor.org/stable/186282. Accessed 27 Mar. 2020.