Should I do a Postdoc if I want an Industry Career? Six things to Consider
Should I do a Postdoc if I want an Industry career? (Six things to consider prior to doing a Postdoc) By Dr Felix Quayson I have had the pleasure of speaking with many graduate students (both master’s and Doctoral) about academia and industry career pathways. A question I hear a lot in these conversations is, “should I do a postdoc if I want an industry career?” In this article, I explain why I advise students to avoid doing a postdoc in that situation. The main reason is that whilst a postdoc experience is usually welcomed in academia, this is not the case outside of it. Thus, do not let your academic advisor pressure you into doing a postdoc if you really want an industry career. Companies like to work with graduates who are focused and want to solve real world problems and know what they really want to do after completing graduate studies. They also like graduates who see themselves as practitioners (being practical) rather than theorists (being theoretical). Below are 6 pieces of advice and things to consider if you are wondering if you should do a postdoc prior to entering an industry career “A postdoc experience makes you work and think like an academic, not an industry professional” Postdoc won’t help you. It will end up hurting you if you do a postdoc but at the same time you prefer to go into industry for career advancement. Getting a master’s or Doctorate is time consuming and it means that you have already spent considerable time (2-8 years) in academia. Now, if you truly want an industry career, it may be time to exit academia and focus your energy on landing your industry job. Getting out of academia will help you see what is out there in the real world and see how that would work for you. Because when you do a postdoc, you are technically still working in academia. Lessons from doing a Postdoc. Postdoc is meant to give you additional research or teaching training and a focused specialization in your field or discipline. You do gain some technical skills and expertise in your field or discipline, which would be relevant to opportunities in academia. However, you would not learn how industry works. And the chances of you getting an industry job with the same technical skills you learned doing a postdoc are slim to none. Your postdoc training may simply not be relevant or meet the demands of an industry career. “when you do a postdoc, you are technically still working in academia” 3. Do you want to become a Professor? Having a postdoc experience can be a great way for you to be competitive in the tenure-track job market in academia. You will receive specialize training, faculty advisor support, research opportunities, managed student research teams, and a focus on your research interests. Remember, however, that all the glories of a postdoc experience would not be helpful or relevant if you want an industry career. Thus, it is better to start early (fresh out of your graduate school graduation or the moment you become a candidate or ABD) to seek for careers in industry. You need to learn how companies work across various industries, and doing a postdoc will prevent you from doing so. “Another important perception is that postdoc teaches to solve academic research problems and not real-world problems” 4. Work patterns. The work that you do in academia and the work that you do in industry are two different things. The academic routines and experience are applicable and relevant almost exclusively in the academic environment! In fact, these routines and habits may prove counterproductive in the industry. A postdoc experience makes you work and think like an academic, not an industry professional. 5. A defined Perception. In the industry, your decision to do a postdoc after completing your graduate degree is seen as you having more interest in academia than industry. Not surprisingly, some may believe that you didn’t really know what you wanted to do after completing your graduate degree. Or maybe you still prefer an academic job but are just applying to their company because you were unsuccessful in the academia? Another important perception (and assumption) is that postdoc teaches to solve academic research problems and not real-world problems. “The academic routines and experience are applicable and relevant almost exclusively in the academic environment!“ 6. Volunteer in the industry. Considering the above, I advise you to do some volunteering outside academia if you decided to pursue a career in the industry. Volunteering may help you learn how the industry works and how decisions are made there. This experience may also help you understand the difference between working in academia and outside of it, and help you decide on the next move. About the author: Felix Quayson has earned degrees in Education and Health, and professional certification in teaching English as a second/foreign language, research certifications from The Collaborative Institutional Training Initiative (CITI Program), and professional credentials in Education at Harvard Graduate School of Education where he is completing Certificate in Media & Technology for Education. Felix research interests is in Education Science focusing on workforce education, EdTech, STEM, & Higher Ed.
Living with machines that can tell right from wrong? An introduction to key ideas in ethical reasoning
Living with machines that can tell right from wrong? An introduction to key ideas in ethical reasoning By Huayi Huang Cite as: Huang, Huayi (August 2021). Living with machines that can tell right from wrong? An introduction to key ideas in ethical reasoning. https://drkriukow.com/living-with-machines-that-can-tell-right-from-wrong-an-introduction-to-key-ideas-in-ethical-reasoning/ Introduction In a previous article, we started exploring the evolution of artificial intelligence ideas in context of the history of modern computer science. Making the distinction between ‘strong variants’, and ‘narrow/weak variants’ in ongoing efforts to replace and mechanise parts of our natural intelligence. Whilst progress continues in “the theory and development of computer systems able to perform tasks normally requiring human intelligence” (in search of ideas and technologies for strong AI), guidelines around ethics involving artificial beings are emerging as a research area of increasing interest. To help us unpack this emerging debate, lets familiarise ourselves with some foundational ideas others have considered in the past, in building our own framework for reasoning about the ethics of our achievements and actions. In this article we work with the idea of ethics being about finding our own frameworks for judgement for determining right or wrong, and cover some of the key ingredients offered for doing this from the ethics literature of the past. In delving deeper into some key ideas from the (documented) history of ethical thinking, some of these ideas might help us in considering current debates around the artificial beings potentially created through our technological developments. “Whilst progress continues in “the theory and development of computer systems able to perform tasks normally requiring human intelligence” (in search of ideas and technologies for strong AI), guidelines around ethics involving artificial beings are emerging as a research area of increasing interest.” 1. Key ‘ethical ingredients’ in judging decision making or relationship dynamics between beings In evaluating and reflecting on how we ought to interact with each other and make decisions about relationships, we might talk about the ethics of utility, rights and responsibilities, virtues, and contracts. 1.1 The ethics of utility (utilitarian ethics) “The end justifies the means” is an idea familiar to many of us, as we seek to make a positive difference in the world. Starting with the idea of evaluation centered around potential or actual utility, achieving the ‘right outcomes’ is at the heart of which decisions and relationships are considered right or wrong. For example, in engaging the services of professionals from a cheap garage to fix our car at a budget price (the desired end), or doing what we can against ‘wrong outcomes’ in context of a particular time or place, e.g. when healthcare outcomes become unnecessarily unsafe and harmful, rather than healing – as I became aware of during my PhD (on Development of New Methods to Support Systemic Incident Analysis of patient safety incidents). In moral reasoning around utility, a being tells right from wrong by focusing on the rightness of the anticipated or actual outcomes, rather than their own or others’ conduct leading up to the outcomes. If the priorities of a country is in getting the ‘right amount’ in its aggregate level economic outcome indicators for example, then whether policies of herd immunity or alternative approaches are better for responding to COVID-19 is of secondary and peripheral importance – so long as the aggregate level ‘right amount’ is achieved. 1.2 Which behaviours are ethical? In evaluating how we ought to behave with each other then, ethicists speak about both duties and rights which govern how we do things, and in reflecting on how we ought to behave in interacting with other beings. The ethics of duty is the study of deontontology or deontological ethics – in reasoning through the responsibilities we have to ourselves and others. Counterpart to this are the ethics of rights – such as those codified in human right conventions and legislation for example. In reasoning about a particular duty or right and its associated behaviours, we may focus less on whether the right or wrong outcomes are achieved, or achievable. For example, whether a parent is fulfilling their duty of care towards their child is arguably less of a outcomes-based judgment, than the duty of our water suppliers to fix the water supply when it goes down (at least in the UK). Some of our codified rights – such as the General Data Protection Regulation (GDPR) legislation recently in the UK – can also receive more or less media attention at times, than other rights we convinced ourselves to have because we are human beings. Should artificial being have the human right to favourable conditions of work too? Currently no one really knows! But in reasoning about the ethics of decision making or relationship dynamics involving other human beings as a means towards some greater goal (e.g. of eradicating world poverty, or to achieve the company bottom line), we do know that the ‘right ends’ do not always justify their means of potential or actual accomplishment. 1.3 Do we have intrinsic virtues as human or artificial beings? Virtues based ethical frameworks all rest on the assumption that as human beings, we have virtues intrinsically (i.e. from NATURE), or can be taught to become virtuous – in how we relate to, think about, or behave towards other beings we co-exist with. Aiding children to learn to become beings with graces and courtesies (https://www.berkshiremontessori.org/msb-blog/basics-grace-and-courtesy), is one example from ethical principles codified as a core element of Montessori Education – of the right way to interact with fellow beings in the world (ranging from other animals to fellow human beings). Turning to broader developments in the world of science and scholarship – the advanced academic training of 19th and early 20th century researchers, was in the hope and ideal of producing beings intrinsically competent and trustworthy in their roles in investigations advancing the public rather than private good, and in disseminating the results from (re)examining the human condition. But darker chapters from our more recent history, has led to a general loss of
5 principles of psychological wellbeing in human development
5 principles of psychological wellbeing in human development By Huayi Huang huayi.the.researcher@gmail.com Cite as: Huang, Huayi (August 2021). 5 principles of psychological wellbeing in human development. https://drkriukow.com/5-principles-of-psychological-wellbeing-in-human-development/ What helps us grow into the changes we see around us? In a recent Quora post, somebody asked about some principles of human development. I’d like to share five principles here today in reply, from scientific theory I’ve been involved with in my recent work. Theorising of course plays a central role in qualitative research (as it does in many other areas of knowledge growth), but the ideas below have far wider implications for our lives. The first 3 principles come from Self Determination Theory, a theory of the psychology of human motivation, wellbeing, and those forces driving change in our behaviour. The second 2 principles come from Montessori Theory, a theory of how we develop, learn, and come to possess an individual and collective spirit for facing our challenges. Both theories are currently alive and well, and widely applied to explain what we observe across both formative and later developmental settings of the human being. Both theories agree on the existence of innate human needs, as we live and co-develop alongside the changes we see in our worlds. From Self Determination Theory we have proposals identifying 3 basic human tendencies, needing to be satisfied for us to psychologically flourish and grow. From Montessori theory, we learn about two foundations keys: in becoming a human being who continues to mentally thrive, in spite of the bumps, bruises or disappointments making an appearance in the long road of life. “From Self Determination Theory we have proposals identifying 3 basic human tendencies, needing to be satisfied for us to psychologically flourish and grow. From Montessori theory, we learn about two foundations keys: in becoming a human being who continues to mentally thrive, in spite of the bumps, bruises or disappointments making an appearance in the long road of life.” From Self Determination Theory we learn that reasons for what we do often relate to the frustration or satisfaction of our basic psychological needs, in the environments we move in. The first of these 3 basic needs is competence (P1). In the sense of whether our personal or professional environments, for example, are supportive or debilitating of our belief in basically ‘doing well’ – in the life activities we hold most dear. Autonomy (P2) in the context of self-determination is about the sense of personal control and choice in those activities we engage in. Slavery, for example, has been widely regarded as a setting in which the human being’s will and opportunity to choose is greatly reduced and curtailed. In contrast, a key ideal of much of Western culture – to (autonomously) choose those thoughts and actions we engage in for ourselves. But to be thinking and doing well for ourselves is not sufficient, in cultivating a deeper sense of personal psychological wellbeing. For example, the social isolation witnessed during the recent COVID pandemic may have challenged some of us more than others. Self Determination Theory proposes that one of possible reasons for this challenge is our innate psychological need to connect and relate (P3) to other human beings. The socially excluded individual is unlikely to be happy or psychologically well. As citizens during these difficult times of uncertainty, social unrest, and difficulty, perhaps all becoming a little more responsible in extending a hand of inclusion to others where we can. Through study of the psychology of the early years in connection with her Montessori pedagogy, Dr Silvana Montanaro discovered the importance of both developing a basic faith in ourselves (P4) and a basic faith in our environments (P5). A basic faith in ourselves, to figure things out – is a recurring theme in much of the self help literature. For Dr Montanaro (a senior Montessori trainer and medical practitioner who extensively studied the human being of 0-3), this basic trust in both ourselves and the world around us is a key aspect in the development of our first personalities; hopefully role modelled as part of the self-care being practiced around us in our early years. Complementing the sense of thinking and doing well for ourselves as well as others, then, a basic confidence in ourselves to succeed in figuring out our world, and in being rewarded for our perseverance in this striving. “the recent COVID pandemic may have challenged some of us more than others. Self Determination Theory proposes that one of possible reasons for this challenge is our innate psychological need to connect and relate to other human beings.” What does it mean for me? These principles from Montessori theory identify 2 keystones (trusting in our environment as well as ourselves) to unlocking a healthy outlook and foundation – in tacking our challenges. But the 3 principles from Self Determination Theory serve to guide us, in searching for natural and human environments that enhance our sense of competence, autonomy, and belonging with others. Losing faith in ourselves, for example, is often accompanied by doubts arising about our competence – in the basic and more elaborate activities key to our daily life. Losing trust in the things, others, and opportunities in our environments, in turn, can lead to a strong sense of isolation and disengagement, from the wealth of experiences, stories, challenges, and successes from our shared human story and history. These 5 principles of human development have been shown to be important, in helping us choose environments that encourage rather than suppress our natural tendencies towards growth in how we think about the world. To adapt to, seek, and thrive from a basic faith in ourselves to become a part of those trustworthy natural and human environments. Your trust in these principles of human development might serve as a guiding light and motivation, in how you relate to the stories of others in the present. Reinvigorating our best selves, in tackling those difficult personal or professional relationships
On observational learning in research
On Observational Learning in Research By Huayi Huang huayi.the.researcher@gmail.com Cite as: Huang, Huayi (May 2021). On Observational Learning in Research. https://drkriukow.com/elementor-3389/ To participate or to observe, according to Raymond L. Gold In 1958, a professor of sociology published a landmark paper still influential for field researchers today – around four types of observational roles (Gold 1958, see fig 1 below). His proposal teaches us to treat the relationship between participant and non-participant observations not so much as a dichotomy, but as falling along a spectrum of 4 key types. On the one end, the “complete participant” – totally involved in ‘doing’ the life of the group, and is part of the ‘normal life environment’ of the researched. On the other end of the spectrum – the “complete observer” – who remains aloof and uninvolved in the social setting and tasks of the group (and whose presence may not be necessarily known to those being watched). Figure 1: Four observational roles for the field researcher The complete participant seeks to adopt the ‘role of the native’ – in the sense of seeking to embrace and appearing to identify with the life of those being observed. In fully engaging with their social life, the notes, recordings, reflections, and documented learning about the observational setting is deprioritised – at least in study participants’ exposure to these field work(er) activities. In essence the sense of ‘self’ of the complete participant, becomes very much part of the normal fabric of study participants’ personal or professional lives. Like the complete participant the Participant-as-Observer shares the need to be ‘in role’, in their participation in the ‘normal’ life and tasks observed; in contrast to their relationship with the complete participant, study participants are typically aware of the dual roles played by the Participant-as-Observer, and gradually adjusts their expectations and habits accordingly for this slight intrusion into their normal way of life. To a field worker engaged in this second type of relationship with that which they are observing, it is okay to eg. get out a small, unobtrusive notepad or device to record a few informal reminders. These reminders might be elaborated on later, out of sight – but with broad participant consent and knowledge that their behaviours and interactions are being recorded for research purposes. Study participants may be part of formal observations also (eg. in seeing an interviewer write down notes during complementary study interviews), and the informal participation by the researcher in the life of the setting or group (eg. office parties) is no longer the only thing in the awareness of the participant. In Participant-as-Observer field work, any dilemmas or tensions in the dual roles of participant-observer are mostly resolved in favour of the demands for participation by the researcher: in playing the part of a novice taxi driver (in transport field studies) for example, or trying to act as an organisational colleague as part of a secondment role (in an organisational study). The development of longitudinal relationships (such as friendships) still occurs, like in the case of the complete participant, but the relationships between the field worker and his/her friends from the setting must develop to accommodate the additional role of the scientific observer; in this role we are expected to take notes, and balance the ongoing tension in relating sometimes as an ‘insider’ and active informal participant to the particular things, events, and ideas being expressed and experienced, and sometimes as an ‘outside observer’ with a more impartial and critical eye. In more cross-sectional learning contexts (eg survey like), you might engage with your fieldwork as an Observer-as-Participant, leading with the observation part of this dual role. Like in a survey, the contact between the researcher and the researched is relatively brief, and fragmented with respect to the entity, process, experience(s) etc. being studied. Field researchers working in complete participant or Participant-as-Observer roles have been long concerned with risks of a researcher “going native”[1], which are minimised in the role of Observing-as-a-Participant. Accompanying this benefit however is the limitation of an increased risk of misunderstanding, or more typically under-documentation: of the full complexities, tensions, contexts, coherence, etc. of the individual lives sampled in creating a cross-sectional data set. Whilst quantified ‘instrument validity’ approaches seek to alleviate possible misunderstandings – something like census data for example is far from a nuanced understanding of the full breadth and depth of our individual day to day challenges. In adopting a complete observer role then, the field researcher again simplifies his life down to 1 primary role, relating to the lives and experiences of co-participants in the knowledge production of a study as just a learner (about these experiences). Unlike the complete participant, the complete observer does not do much…other than focusing on learning through observation. This complete observer role comes with significant detachment and distance built in – in relation to the phenomena, setting, tasks, groups, etc being studied. But researchers in this role also risk being limited to overly ethnocentric evaluation of other cultures, for example in application of the preconceptions originating in the standards and customs of their own culture – in pre-judging rather than describing things of significance for the study. [1] Referring to the experience where a researcher uncritically accepts the views they learn from the ‘field’ as their own, because they have become too similar in outlook, worldviews, etc. to their study participants. “On the one end, the “complete participant” – totally involved in ‘doing’ the life of the group, and is part of the ‘normal life environment’ of the researched. On the other end of the spectrum – the “complete observer” – who remains aloof and uninvolved in the social setting and tasks of the group” Descriptive or judgemental observations? As we go through life, we learn to reason in our own ways about the world – based on what we see around us and the experiences we have. Along with the patterns of reasoning we come to know and apply in our learning projects (in our deductive,
An introduction to Qualitative Research Artificial Intelligence Technologies
An introduction to Qualitative Research Artificial Intelligence Technologies By Huayi Huang Cite as: Huang, Huayi (16 March 2021). An introduction to Qualitative Research Artificial Intelligence Technologies. https://drkriukow.com/an-introduction-to-qualitative-research-artificial-intelligence-technologies/ Drkriukow.com Introduction To understand the relationship between Qualitative Research and Artificial Intelligence (AI) technologies, it helps to start with some key ideas in the development of AI within modern computer science. We follow this, by going a little into its relationship with current views on software supported data analysis within qualitative research. Within computer science knowledge communities, AI has been historically subdivided into STRONG and WEAK variants (‘narrow’ AI in current terms). Both sets of idea still exert considerable influence on current thinking, in the general thrust of ongoing AI research programmes for example. To ‘strong AI’ researchers, the performance of ‘normal’ or ‘expert’ humans – particular in their thinking and response to the natural world – is assumed to be the gold standard, in seeking to achieve the ideal of human level AI through their research programmes. The general aim for strong AI, is to remould ‘mechanised’ intelligence in the image of human beings, and to see the natural intelligence we exhibit in the day to day, as a source of insight for developing more human like traits, features, skills, etc. in our technologies and machines. As summarised nicely by Forbes (2019) recently, Artificial General Intelligence (AGI) in essence searches for machines that can successfully: (1) generalise knowledge from one domain to another, in taking knowledge originating from one area and trying to apply it elsewhere, (2) make plans for the future based on existing knowledge and experiences, and (3) adapt to the environment as changes occur. All of this based on our very human capabilities of reasoning, puzzle solving, planning, and coming to a personal and collective common sense about the world. Notably for researchers working on ‘weak AI’ technologies – the general strategy was instead to start with what exists in the current machine. The work of narrow AI then, was to argue for the advance and expansion of capabilities existing in the machine; at a societal level arguably valuing technological over human development in various (narrow) domains of performance.[1] In this light, cited successes of ‘narrow AI’: such as image and speech recognition, AI powered chatbots, self driving cars, and Optical Character Recognition, do not so much progress artificial agents and intelligence(s) along a one dimensional ‘ladder’ or ‘spectrum’; but instead function to replace multi-dimensional facets of the natural intelligence we’ve inherited in our evolution as human beings (eg. recognising faces and the content of spoken word, engaging in conversation, etc.). Whilst not explicit in the stated aims of narrow/weak AI research programmes, the increasing integration of mechanical traits, features, skills, and capabilities to support the way we reason about the people and things around us sometimes attract interesting side effects – where the full potential of the human being (for self-actualization for example) may be remoulded in the image (and limitations) of the artificial intelligence technology. This can be seen in the common resignation and experience of needing to perform things a particular way (often a little awkward!), to accomplish some broader purpose in collaboration with some technological tool or system. To qualitative researchers, it is uncontroversial that definitions (of visions, ideas, projects, situations, etc.) are important to explore and debate, since they often help or hinder us in accomplishing meaningful work in our personal or professional lives. As Maxwell highlights for example, a fundamental insight from symbolic interactionism is in the fact that people act in accordance with their definitions of situations, so fruitful explanations[2] are therefore dependent on understanding these definitions. In seeking to understand the relationship between Qualitative Research and AI technologies then, lets us start by revisiting some key definitions from the history of computer science, and update these definitions of AI for the present. To help us in this, I’ve added a small commentary next to each set of quotes, to elaborate on their meaning for us as researchers in current times: “From what I see, we do not yet live in a world where machines can simulate every aspect of human learning or intelligence, but even if we could enable such an ‘AI singularity’, what of our place as human beings in such a world?” In search of ideas and technologies for strong AI “The study [of AI] is to proceed on the basis of the conjecture that every aspect of learning or any other feature of [human] intelligence can in principle be so precisely described that a machine can be made to simulate it” (McCarthy, Minsky, Rochester, & Shannon, 1955) Commentary: What a grand definition! In this we see the idea of remoulding ‘mechanised’ intelligence in the image of human beings, in the search for machines simulating human intelligence. McCarthy et al’s ‘conjecture’ here highlights what is in reality a body of proposals being intensely elaborated on in the current decade: about a general relationship (of simulation-ability) between aspects of human learning or intelligence, and the capabilities of our mechanical counterparts. We can of course choose to accept or reject these propositions as an accurate description of the relationships we actually see in our own experiences, with learning, natural intelligence, and artificial simulations of this intelligence. From what I see, we do not yet live in a world where machines can simulate every aspect of human learning or intelligence, but even if we could enable such an ‘AI singularity’, what of our place as human beings in such a world? “The study of computations that make it possible to perceive, reason, and act” (Winston, 1992). “The exciting new effort to make computers think…machines with minds, in the full and literal sense” (Haugeland, 1985). Commentary: These definitions bring to mind the idea that as human beings we learn to perceive, reason, and act in an uncertain and changing world as children, and get a little better at doing this as we grow in age and worldly experience. Regardless of our
Steps in research (Comic)
