If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
A day after her interview for a part-time job at Target last year, Dana Anthony got an email informing her she didn't make the cut. Anthony didn't know why -- a situation common to most job seekers at one point or another. But she also had no sense at all of how the interview had gone, because her interviewer was a computer. More job-seekers, including some professionals, may soon have to accept impersonal online interviews where they never talk to another human being, or know if behind-the-scenes artificial-intelligence systems are influencing hiring decisions. Demand for online hiring services, which interview job applicants remotely via laptop or phone, mushroomed during the COVID-19 pandemic and remains high amid a perceived worker shortage as the economy opens back up.
All humans have bias, unconscious or otherwise – the risks and effects of which have never been more apparent than in recruitment. As such, it seems reasonable to hope that technology holds the key to achieving fairer outcomes in hiring decisions. For those committed to making the process more inclusive and organisations more diverse, the potential and ever increasing possibilities for technology as part of recruitment can appear limitless. As the use of AI in recruitment continues to hit the headlines, automation in recruitment has become more widespread, transforming who and how companies recruit. Although the appeal of AI is clear (and its use can be transformative in recruitment), it is important to reappraise exactly what technology can change for the better right now. By taking a more realistic look at the technology, we can recalibrate our expectations and understand the role we have to play in driving meaningful change.
Following the success of spoken dialogue systems (SDS) in smartphone assistants and smart speakers, a number of communicative robots are developed and commercialized. Compared with the conventional SDSs designed as a human-machine interface, interaction with robots is expected to be in a closer manner to talking to a human because of the anthropomorphism and physical presence. The goal or task of dialogue may not be information retrieval, but the conversation itself. In order to realize human-level "long and deep" conversation, we have developed an intelligent conversational android ERICA. We set up several social interaction tasks for ERICA, including attentive listening, job interview, and speed dating. To allow for spontaneous, incremental multiple utterances, a robust turn-taking model is implemented based on TRP (transition-relevance place) prediction, and a variety of backchannels are generated based on time frame-wise prediction instead of IPU-based prediction. We have realized an open-domain attentive listening system with partial repeats and elaborating questions on focus words as well as assessment responses. It has been evaluated with 40 senior people, engaged in conversation of 5-7 minutes without a conversation breakdown. It was also compared against the WOZ setting. We have also realized a job interview system with a set of base questions followed by dynamic generation of elaborating questions. It has also been evaluated with student subjects, showing promising results.
Are you planning to sit for deep learning interviews? Have you perhaps already taken the first step, applied, and sat through the ordeal of several rounds of interviews for a deep learning role and not made the cut? Cracking an interview, especially for a complex role like a deep learning specialist, is a daunting task for most people. Deep learning is a vast field with an ever-changing nature as new developments are rolled out on a regular basis. How can you keep up with the pace? What should you focus on? These are questions every deep learning enthusiast, fresher and even expert has asked themselves at some point.
Decision Tree: Every hiring manager has a set of criteria such as education level, number of years of experience, interview performance. A decision tree is analogous to a hiring manager interviewing candidates based on his or her own criteria. Bagging: Now imagine instead of a single interviewer, now there is an interview panel where each interviewer has a vote. Bagging or bootstrap aggregating involves combining inputs from all interviewers for the final decision through a democratic voting process. Random Forest: It is a bagging-based algorithm with a key difference wherein only a subset of features is selected at random.
Suppose you get a call from the recruiter of your dream company where you have applied for the ML Engineer role. You have set a date and started preparation with an ML study guide like this one or similar. On the day of the interview, you are able to answer all the questions and are confident that you will move onto the onsite stage. However, you get a call from the recruiter saying that they have decided not to go forward. It is not enough to answer the question, because the interviewer wants to see that you have a deep understanding of the topic/question.
Skilled AI workers are being urgently sought. Knowledge and some experience in artificial intelligence and machine learning are the job skills most in demand in 2021. That is confirmed in a survey by Hackerearth of 2,500 developer recruiters and hiring managers reported in a recent account in The Enterprisers Project. The AI field is expansive, with a wide range of skills represented. "This is an incredibly broad field, and not all jobs will require the same skills. As your organization competes for talent, don't let enthusiasm cloud your judgment," stated Rajan Sethuraman, CEO of LatentView Analytics, author of the piece.
This article takes you through some of the machine learning interview questions and answers, that you're likely to encounter on your way to achieving your dream job. But it doesn't have to be this way. These questions are collected after consulting with Machine Learning Certification Training Experts. We'd ask the following types/examples of questions, not all of which are considered pass/fail, but do give us a reasonable comprehensive picture of the candidate's depth in this area. During her career she has interviewed over a 100 candidates.
Machine Learning topics are highly technical and straight forward when discussed. What I mean is that when questions are asked, there tends to be little to no ambiguity to answers that could be given. You either know what you are talking about or you don't. In some interview scenarios, there's little to no room to have an extended discussion around ML topics, especially when an interview feels like an interrogation with the several "What is" and "How Do I" type questions. There's nothing wrong with having direct answers when asked questions such as "What is a neural network" or "How do you prevent a network from overfitting".
To cover the complete interview process, I have divided this post into separate events based on the timeline. This will help you to evaluate the process and preparation time required for each stage of the interview better. If you are looking to interview for a similar position, it is important to evaluate the profiles which get picked for the interview process. Now, I don't know exactly how my profile stood in the pool of candidates, but I am sharing my resume as a sample profile that got picked for such interviews. As you can see, I had completed 4 years of my Ph.D. by this time, publishing majorly in the areas of Machine Learning, Data Visualization, and Computer Vision.