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George Ng
PINBOARD SUMMARY

Artificial Intelligence and automation are already disrupting jobs, but mostly repetitive ones

In 10 seconds? Artificial Intelligence is getting very good at making decisions analysing large datasets. In practice it means that AI and automation are threatening a lot of current workplaces, especially those that involve repetitive tasks.

Any particular industries where AI could replace humans? Depending of the surveys you read, there are varying levels of “doomsday thinking” about the “Fourth Industrial Revolution”. However most experts agree that Artificial Intelligence and automation can affect nearly all industries. According to a recent UK survey, AI technologies are already being implemented in one third of industries.

So what are the jobs under threat and how many? PriceWaterHouseCoopers estimates that 30-40% of jobs will be affected in the developed world. It is predicted that AI will be able to replace many white collar jobs, such as truck drivers, retail workers but will also be able to write high school essays and translate documents. Additionally, algorithms threaten many financial advisor positions. The Australian financial startup, Stockpot boasts that its algorithm “can provide cheaper and better financial advice than people”.

So AI is all bad news? Not necessarily. In healthcare, AI can both help overstretched GPs make better decisions - for example by using deep learning methods to analyse mammograms for breast cancer screening -, predicting illnesses or by controlling health management systems. Additionally, robots are being introduced to help care for the elderly or autistic children and assist surgeons.

Is there anything we can do to save our jobs from the robots? We can future-proof our careers. Learning coding seems to be a safe bet or moving toward positions that require strategic and critical thinking or involving ‘case by case’ scenarios. Doctors, nurses, lawyers and financial planners seem to be safe, but they will also rely on AI in their work.

So, there is still hope for us? Yes, not everyone is pessimistic about the future. There are those who argue, that AI and automation will create new jobs and opportunities for software engineers, programmers, algorithm designers, AI trainers, ethicists and lawyers. They also insist that human analytical teams providing context and critical judgement in decision making will need to complement AI. Concerns about security may also slow down the penetration of AI as many IT managers are worried about being vulnerable to cyberattacks.

17 ITEMS PINNED

Artificial intelligence techniques for driving safety and vehicle crash prediction

Abstract: Abstract Accident prediction is one of the most critical aspects of road safety, whereby an accident can be predicted before it actually occurs and precautionary measures taken to avoid it. For this purpose, accident prediction models are popular in road safety analysis. Artificial intelligence (AI) is used in many real world applications, especially where outcomes and data are not same all the time and are influenced by occurrence of random changes. This paper presents a study on the existing approaches for the detection of unsafe driving patterns of a vehicle used to predict accidents. The literature covered in this paper is from the past 10 years, from 2004 to 2014. AI techniques are surveyed for the detection of unsafe driving style and crash prediction. A number of statistical methods which are used to predict the accidents by using different vehicle and driving features are also covered in this paper. The approaches studied in this paper are compared in terms of datasets and prediction performance. We also provide a list of datasets and simulators available for the scientific community to conduct research in the subject domain. The paper also identifies some of the critical open questions that need to be addressed for road safety using AI techniques.AbstractAccident prediction is one of the most critical aspects of road safety, whereby an accident can be predicted before it actually occurs and precautionary measures taken to avoid it. For this purpose, accident prediction models are popular in road safety analysis. Artificial intelligence (AI) is used in many real world applications, especially where outcomes and data are not same all the time and are influenced by occurrence of random changes. This paper presents a study on the existing approaches for the detection of unsafe driving patterns of a vehicle used to predict accidents. The literature covered in this paper is from the past 10 years, from 2004 to 2014. AI techniques are surveyed for the detection of unsafe driving style and crash prediction. A number of statistical methods which are used to predict the accidents by using different vehicle and driving features are also covered in this paper. The approaches studied in this paper are compared in terms of datasets and prediction performance. We also provide a list of datasets and simulators available for the scientific community to conduct research in the subject domain. The paper also identifies some of the critical open questions that need to be addressed for road safety using AI techniques.

Pub.: 01 Oct '16, Pinned: 03 Jul '17

Mistaking Minds and Machines: How Speech Affects Dehumanization and Anthropomorphism.

Abstract: Treating a human mind like a machine is an essential component of dehumanization, whereas attributing a humanlike mind to a machine is an essential component of anthropomorphism. Here we tested how a cue closely connected to a person's actual mental experience-a humanlike voice-affects the likelihood of mistaking a person for a machine, or a machine for a person. We predicted that paralinguistic cues in speech are particularly likely to convey the presence of a humanlike mind, such that removing voice from communication (leaving only text) would increase the likelihood of mistaking the text's creator for a machine. Conversely, adding voice to a computer-generated script (resulting in speech) would increase the likelihood of mistaking the text's creator for a human. Four experiments confirmed these hypotheses, demonstrating that people are more likely to infer a human (vs. computer) creator when they hear a voice expressing thoughts than when they read the same thoughts in text. Adding human visual cues to text (i.e., seeing a person perform a script in a subtitled video clip), did not increase the likelihood of inferring a human creator compared with only reading text, suggesting that defining features of personhood may be conveyed more clearly in speech (Experiments 1 and 2). Removing the naturalistic paralinguistic cues that convey humanlike capacity for thinking and feeling, such as varied pace and intonation, eliminates the humanizing effect of speech (Experiment 4). We discuss implications for dehumanizing others through text-based media, and for anthropomorphizing machines through speech-based media. (PsycINFO Database Record

Pub.: 12 Aug '16, Pinned: 01 Jun '17

Utilization of machine learning for prediction of post-traumatic stress: a re-examination of cortisol in the prediction and pathways to non-remitting PTSD.

Abstract: To date, studies of biological risk factors have revealed inconsistent relationships with subsequent post-traumatic stress disorder (PTSD). The inconsistent signal may reflect the use of data analytic tools that are ill equipped for modeling the complex interactions between biological and environmental factors that underlay post-traumatic psychopathology. Further, using symptom-based diagnostic status as the group outcome overlooks the inherent heterogeneity of PTSD, potentially contributing to failures to replicate. To examine the potential yield of novel analytic tools, we reanalyzed data from a large longitudinal study of individuals identified following trauma in the general emergency room (ER) that failed to find a linear association between cortisol response to traumatic events and subsequent PTSD. First, latent growth mixture modeling empirically identified trajectories of post-traumatic symptoms, which then were used as the study outcome. Next, support vector machines with feature selection identified sets of features with stable predictive accuracy and built robust classifiers of trajectory membership (area under the receiver operator characteristic curve (AUC)=0.82 (95% confidence interval (CI)=0.80-0.85)) that combined clinical, neuroendocrine, psychophysiological and demographic information. Finally, graph induction algorithms revealed a unique path from childhood trauma via lower cortisol during ER admission, to non-remitting PTSD. Traditional general linear modeling methods then confirmed the newly revealed association, thereby delineating a specific target population for early endocrine interventions. Advanced computational approaches offer innovative ways for uncovering clinically significant, non-shared biological signals in heterogeneous samples.

Pub.: 23 Mar '17, Pinned: 01 Jun '17

Use of a machine learning framework to predict substance use disorder treatment success.

Abstract: There are several methods for building prediction models. The wealth of currently available modeling techniques usually forces the researcher to judge, a priori, what will likely be the best method. Super learning (SL) is a methodology that facilitates this decision by combining all identified prediction algorithms pertinent for a particular prediction problem. SL generates a final model that is at least as good as any of the other models considered for predicting the outcome. The overarching aim of this work is to introduce SL to analysts and practitioners. This work compares the performance of logistic regression, penalized regression, random forests, deep learning neural networks, and SL to predict successful substance use disorders (SUD) treatment. A nationwide database including 99,013 SUD treatment patients was used. All algorithms were evaluated using the area under the receiver operating characteristic curve (AUC) in a test sample that was not included in the training sample used to fit the prediction models. AUC for the models ranged between 0.793 and 0.820. SL was superior to all but one of the algorithms compared. An explanation of SL steps is provided. SL is the first step in targeted learning, an analytic framework that yields double robust effect estimation and inference with fewer assumptions than the usual parametric methods. Different aspects of SL depending on the context, its function within the targeted learning framework, and the benefits of this methodology in the addiction field are discussed.

Pub.: 11 Apr '17, Pinned: 01 Jun '17

Artificial Intelligence in Medicine

Abstract: Publication date: Available online 11 January 2017 Source:Metabolism Author(s): Pavel Hamet, Johanne Tremblay Artificial Intelligence (AI) is a general term that implies the use of a computer to model intelligent behaviour with minimal human intervention. AI is generally accepted as having started with the invention of robots. The term derives from the Czech word robota, meaning biosynthetic machines used as forced labour. In this field, Leonardo Da Vinci's lasting heritage is today's burgeoning use of robotic-assisted surgery, named after him, for complex urologic and gynecologic procedures. Da Vinci's sketchbooks of robots helped set the stage for this innovation. AI, described as the science and engineering of making intelligent machines, was officially born in 1956. The term is applicable to a broad range of items in medicine such as robotics, medical diagnosis, medical statistics, and human biology—up to and including today's “omics”. AI in medicine, which is the focus of this review, has two main branches: virtual and physical. The virtual branch includes informatics approaches from deep learning information management to control of health management systems, including electronic health records, and active guidance of physicians in their treatment decisions. The physical branch is best represented by robots used to assist the elderly patient or the attending surgeon. Also embodied in this branch are targeted nanorobots, a unique new drug delivery system. The societal and ethical complexities of these applications require further reflection, proof of their medical utility, economic value, and development of interdisciplinary strategies for their wider application.

Pub.: 11 Jan '17, Pinned: 01 Jun '17