I am currently freelancing for multiple companies as a Machine Learning Developer Associate. I couldn't even contemplate something like this a few months back and it is all thanks to the people and amazing mentors at Smartknower who helped me come out of my fear of having a lack of skills. The practical experience was also top-notch with projects and assessments that kept me in the loop of how much I have learnt and know and how I can implement them to gain good results in the future which is currently helping me in the industry.

Despite the dreaded, looming uncertainty felt throughout 2020, the prevalence of Artificial Intelligence (AI) has been undeniable. Over the last four years, AI specialists reportedly represent the fastest-growing role in the United States. According to recent data from job site Indeed, jobs in AI have seen a recent explosion with a steady hike over the last five years. The report notes that AI job postings have gone up consistently over the past two years, with a 46% hike between 2018-2019, and a 51% spike between 2019-2020. The dramatic increase in job openings within AI hasn't gone unnoticed by savvy job seekers in the market.

Exam cheating is as ancient a practice as education itself. Back in ancient China, cheating on the Imperial exams was a serious offense. And yet a Qing dynasty cheatsheet--in the shape of a handkerchief with 10,000 symbols in microscopic writing--is on display at the Minneapolis Institute of Arts, showing that students have always been inclined to borrow knowledge and ideas. Exam cheating is as ancient a practice as education itself. Historically, students could attempt to use their own handwriting as false proof of originality, but now the digital age has changed that.

Earlier this year, the independent research organisation of which I am the Director, London-based Ada Lovelace Institute, hosted a panel at the world's largest AI conference, CogX, called The Ethics Panel to End All Ethics Panels. The title referenced both a tongue-in-cheek effort at self-promotion, and a very real need to put to bed the seemingly endless offering of panels, think-pieces, and government reports preoccupied with ruminating on the abstract ethical questions posed by AI and new data-driven technologies. We had grown impatient with conceptual debates and high-level principles. And we were not alone. It supersedes the two waves that came before it: the first wave, defined by principles and dominated by philosophers, and the second wave, led by computer scientists and geared towards technical fixes.

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Cerego is an adaptive learning platform that uses artificial intelligence and machine learning to scale proven cognitive science and make learning possible for anyone. The proprietary learning engine at the heart of the platform continuously adapts and enhances the learning experience to meet every individual's needs, while predictive analytics deliver actionable data to educators and managers. By improving the learning experience, Cerego aims to unlock the promise of education and training, ultimately helping people reach their full potential.

The aim of all Question Answering (QA) systems is to be able to generalize to unseen questions. Current supervised methods are reliant on expensive data annotation. Moreover, such annotations can introduce unintended annotator bias which makes systems focus more on the bias than the actual task. In this work, we propose Knowledge Triplet Learning (KTL), a self-supervised task over knowledge graphs. We propose heuristics to create synthetic graphs for commonsense and scientific knowledge. We propose methods of how to use KTL to perform zero-shot QA and our experiments show considerable improvements over large pre-trained transformer models.

After a surge in popularity of supervised Deep Learning, the desire to reduce the dependence on curated, labelled data sets and to leverage the vast quantities of unlabelled data available recently triggered renewed interest in unsupervised learning algorithms. Despite a significantly improved performance due to approaches such as the identification of disentangled latent representations, contrastive learning, and clustering optimisations, the performance of unsupervised machine learning still falls short of its hypothesised potential. Machine learning has previously taken inspiration from neuroscience and cognitive science with great success. However, this has mostly been based on adult learners with access to labels and a vast amount of prior knowledge. In order to push unsupervised machine learning forward, we argue that developmental science of infant cognition might hold the key to unlocking the next generation of unsupervised learning approaches. Conceptually, human infant learning is the closest biological parallel to artificial unsupervised learning, as infants too must learn useful representations from unlabelled data. In contrast to machine learning, these new representations are learned rapidly and from relatively few examples. Moreover, infants learn robust representations that can be used flexibly and efficiently in a number of different tasks and contexts. We identify five crucial factors enabling infants' quality and speed of learning, assess the extent to which these have already been exploited in machine learning, and propose how further adoption of these factors can give rise to previously unseen performance levels in unsupervised learning.

The advent of deep learning (DL) has fundamentally changed the landscape of modern software. Generally, a DL system is comprised of several interconnected computational units that form "layers" which perform mathematical transformations, according to sets of learnable parameters, on data passing through them. These architectures can be "trained" for specific tasks by updating the parameters according to a model's performance on a labeled set of training data. DL represents a fundamental shift in the manner by which machines learn patterns from data by automatically extracting salient features for a given computational task, as opposed to relying upon human intuition. These DL systems can be viewed as an inflection point for software development, as they enable new capabilities that cannot be realized cost-effectively through "traditional" software wherein the behavior of a program must be specified analytically.

In this thesis we look into programming by example (PBE), which is about finding a program mapping given inputs to given outputs. PBE has traditionally seen a split between formal versus neural approaches, where formal approaches typically involve deductive techniques such as SAT solvers and types, while the neural approaches involve training on sample input-outputs with their corresponding program, typically using sequence-based machine learning techniques such as LSTMs [41]. As a result of this split, programming types had yet to be used in neural program synthesis techniques. We propose a way to incorporate programming types into a neural program synthesis approach for PBE. We introduce the Typed Neuro-Symbolic Program Synthesis (TNSPS) method based on this idea, and test it in the functional programming context to empirically verify type information may help improve generalization in neural synthesizers on limited-size datasets. Our TNSPS model builds upon the existing Neuro-Symbolic Program Synthesis (NSPS), a tree-based neural synthesizer combining info from input-output examples plus the current program, by further exposing information on types of those input-output examples, of the grammar production rules, as well as of the hole that we wish to expand in the program. We further explain how we generated a dataset within our domain, which uses a limited subset of Haskell as the synthesis language. Finally we discuss several topics of interest that may help take these ideas further. For reproducibility, we release our code publicly.