Artificial Intelligence is the process of making use of computers and machines to mimic human perception, decision-making, and other processes to complete a task. Put in other words, AI is

There's no doubt that the data science industry has come along way just in the last ten years, but you might be surprised that there is still a lot of growth potential in existing companies today. Perhaps one big reason for that is that we consistently face a shortage of qualified individuals, but I think another reason is that non-practitioners don't really understand the value that data science and artificial intelligence can bring. They hear the words "AI" or "machine learning" and associate those to Hollywood stereotypes like HAL from 2001: A Space Odyssey or Skynet from the Terminator movies. Of course, data science practitioners recognize that those Hollywood AIs represent a fictionalized potential for Artificial General Intelligence (AGI), but there's a lot more to this space than a talking computer. From random forest classifiers working well with structured data to deep learning working with unstructured data like text or images, there are a lot of different ways a data scientist can bring value to the table.

Its adoption accelerated, and leaders correctly predicted growth in the industry in all aspects. Overall, organisations invested more in Data Science, and there was an upswing in the Data Science jobs. While the median salaries of analytics professionals saw a slight decline at the start of the year, a rising trend was witnessed again in the recent months, which will continue to be the case in the coming year. The inefficiencies of Data Science teams from development to deployment in the real world were observed before but they became even more evident due to the pandemic. The operationalisation and scaling of Machine Learning models through structured frameworks was the talk of 2021. These processes will start getting streamlined in the coming years. The Data Science industry also realised the breadth of roles needed for these deployments. While generalists will continue to be in demand, niche roles will play an important role going forward, especially Data Engineers. Subsequently, the role of education will also evolve. It will become further formalised with more specialisation courses introduced.

Multi-agent reinforcement learning (MARL) is a powerful framework for studying emergent behavior in complex agent-based simulations. However, RL agents are often assumed to be rational and behave optimally, which does not fully reflect human behavior. Here, we study more human-like RL agents which incorporate an established model of human-irrationality, the Rational Inattention (RI) model. RI models the cost of cognitive information processing using mutual information. Our RIRL framework generalizes and is more flexible than prior work by allowing for multi-timestep dynamics and information channels with heterogeneous processing costs. We evaluate RIRL in Principal-Agent (specifically manager-employee relations) problem settings of varying complexity where RI models information asymmetry (e.g. it may be costly for the manager to observe certain information about the employees). We show that using RIRL yields a rich spectrum of new equilibrium behaviors that differ from those found under rational assumptions. For instance, some forms of a Principal's inattention can increase Agent welfare due to increased compensation, while other forms of inattention can decrease Agent welfare by encouraging extra work effort. Additionally, new strategies emerge compared to those under rationality assumptions, e.g., Agents are incentivized to increase work effort. These results suggest RIRL is a powerful tool towards building AI agents that can mimic real human behavior.

As artificial intelligence (AI) technologies become increasingly powerful and prominent in society, their misuse is a growing concern. In educational settings, AI technologies could be used by students to cheat on assignments and exams. In this paper we explore whether transformers can be used to solve introductory level programming assignments while bypassing commonly used AI tools to detect plagiarism. We find that a student using GPT-J [Wang and Komatsuzaki, 2021] can complete introductory level programming assignments without triggering suspicion from MOSS [Aiken, 2000], a widely used plagiarism detection tool. This holds despite the fact that GPT-J was not trained on the problems in question and is not provided with any examples to work from. We further find that the code written by GPT-J is diverse in structure, lacking any particular tells that future plagiarism detection techniques may use to try to identify algorithmically generated code. We conclude with a discussion of the ethical and educational implications of large language models and directions for future research.

Research on both natural intelligence (NI) and artificial intelligence (AI) generally assumes that the future resembles the past: intelligent agents or systems (what we call 'intelligence') observe and act on the world, then use this experience to act on future experiences of the same kind. We call this 'retrospective learning'. For example, an intelligence may see a set of pictures of objects, along with their names, and learn to name them. A retrospective learning intelligence would merely be able to name more pictures of the same objects. We argue that this is not what true intelligence is about. In many real world problems, both NIs and AIs will have to learn for an uncertain future. Both must update their internal models to be useful for future tasks, such as naming fundamentally new objects and using these objects effectively in a new context or to achieve previously unencountered goals. This ability to learn for the future we call 'prospective learning'. We articulate four relevant factors that jointly define prospective learning. Continual learning enables intelligences to remember those aspects of the past which it believes will be most useful in the future. Prospective constraints (including biases and priors) facilitate the intelligence finding general solutions that will be applicable to future problems. Curiosity motivates taking actions that inform future decision making, including in previously unmet situations. Causal estimation enables learning the structure of relations that guide choosing actions for specific outcomes, even when the specific action-outcome contingencies have never been observed before. We argue that a paradigm shift from retrospective to prospective learning will enable the communities that study intelligence to unite and overcome existing bottlenecks to more effectively explain, augment, and engineer intelligences.

Real-time object pose estimation is necessary for many robot manipulation algorithms. However, state-of-the-art methods for object pose estimation are trained for a specific set of objects; these methods thus need to be retrained to estimate the pose of each new object, often requiring tens of GPU-days of training for optimal performance. \revisef{In this paper, we propose the OSSID framework,} leveraging a slow zero-shot pose estimator to self-supervise the training of a fast detection algorithm. This fast detector can then be used to filter the input to the pose estimator, drastically improving its inference speed. We show that this self-supervised training exceeds the performance of existing zero-shot detection methods on two widely used object pose estimation and detection datasets, without requiring any human annotations. Further, we show that the resulting method for pose estimation has a significantly faster inference speed, due to the ability to filter out large parts of the image. Thus, our method for self-supervised online learning of a detector (trained using pseudo-labels from a slow pose estimator) leads to accurate pose estimation at real-time speeds, without requiring human annotations. Supplementary materials and code can be found at https://georgegu1997.github.io/OSSID/

In doing so, however, these approaches sacrifice the sample efficiency and generalizability that a well-specified feature Whether it's semi-autonomous driving (Sadigh et al. 2016), set offers. While using an expressive function approximator recommender systems (Ziebart et al. 2008), or household to extract features and learn their reward combination at once robots working in close proximity with people (Jain et al. seems advantageous, many such functions can induce policies 2015), reward learning can greatly benefit autonomous agents that explain the demonstrations. Hence, to disambiguate to generate behaviors that adapt to new situations or human between all these candidate functions, the robot requires a preferences. Under this framework, the robot uses the person's very large amount of (laborious to collect) data, and this data input to learn a reward function that describes how they prefer needs to be diverse enough to identify the true reward. For the task to be performed. For instance, in the scenario in Fig. example, the human in the household robot setting in Figure 1 1, the human wants the robot to keep the cup away from the might want to demonstrate keeping the cup away from the laptop to prevent spilling liquid over it; she may communicate laptop, but from a single demonstration the robot could find this preference to the robot by providing a demonstration of many other explanations for the person's behavior: perhaps the task or even by directly intervening during the robot's task they always happened to keep the cup upright or they really execution to correct it.

Human infants acquire their verbal lexicon from minimal prior knowledge of language based on the statistical properties of phonological distributions and the co-occurrence of other sensory stimuli. In this study, we propose a novel fully unsupervised learning method discovering speech units by utilizing phonological information as a distributional cue and object information as a co-occurrence cue. The proposed method can not only (1) acquire words and phonemes from speech signals using unsupervised learning, but can also (2) utilize object information based on multiple modalities (i.e., vision, tactile, and auditory) simultaneously. The proposed method is based on the Nonparametric Bayesian Double Articulation Analyzer (NPB-DAA) discovering phonemes and words from phonological features, and Multimodal Latent Dirichlet Allocation (MLDA) categorizing multimodal information obtained from objects. In the experiment, the proposed method showed higher word discovery performance than the baseline methods. In particular, words that expressed the characteristics of the object (i.e., words corresponding to nouns and adjectives) were segmented accurately. Furthermore, we examined how learning performance is affected by differences in the importance of linguistic information. When the weight of the word modality was increased, the performance was further improved compared to the fixed condition.

I give a short introduction to data ethics. My focal audience is mathematicians, but I hope that my discussion will also be useful to others. I am not an expert about data ethics, and my article is only a starting point. I encourage readers to examine the resources that I discuss and to continue to reflect carefully on data ethics and on the societal implications of data and data analysis throughout their lives.