While optimistic about the future, Steve Wozniak is not ready to turn over his identity (nor his Tesla) to artificial intelligence anytime soon. At a conference in Budapest I attended, he referenced deleting his Facebook account because of privacy concerns, and that he no longer believes that a totally autonomous car will happen in his lifetime. But Wozniak retains the passion and enthusiasm for technology and innovation that made him a household name as Apple's co-founder. When he and Steve Jobs started Apple, they were trying to develop a new kind of computer that would improve the user experience beyond what was available at the time. Today, "The Woz" is a brilliant engineer, who keeps his eye on what is happening in technology, digital transformation and entrepreneurship.

Artificial intelligence (AI): the hype is real. But is the impact of AI real? "…the theory and development of computer systems able to perform tasks normally requiring human intelligence, such as visual perception, speech recognition, decision-making, and translation between languages" In essence, AI is the intelligence developed in machines, as opposed to the natural intelligence which is developed in humans. And if the hype is to be believed, AI is here to make your life easier: less complex, less burdensome with decision making, less stressful. Artificial intelligence now plays a major role in how your home works: from your sound system, your toaster, your security, to even your lounge room air temp.

Vancouver, British Columbia--(Newsfile Corp. - December 4, 2019) - HEYBRYAN MEDIA INC. (CSE: HEY) ("HeyBryan") an app that connects home-maintenance Experts to homeowners for help with small tasks around the home, today reported a significant increase in the number of completed tasks in the month of November, leading to increased revenue. These impressive results were fuelled by last month's rapid growth in new Expert acquisition across multiple task categories and geographies. Expert acquisition grew by 167% in November, thanks to HeyBryan's AI-enabled, data-driven programmatic marketing campaign. Using AI-driven platform HeyBryan is able to predictably understand the outcomes of its Facebook and Instagram campaigns, and the results speak for themselves. As HeyBryan continues to add investment to its strategic marketing campaigns, these numbers will continue on this trend.

Structured low-rank (SLR) algorithms are emerging as powerful image reconstruction approaches because they can capitalize on several signal properties, which conventional image-based approaches have difficulty in exploiting. The main challenge with this scheme that self learns an annihilation convolutional filterbank from the undersampled data is its high computational complexity. We introduce a deep-learning approach to quite significantly reduce the computational complexity of SLR schemes. Specifically, we pre-learn a CNN-based annihilation filterbank from exemplar data, which is used as a prior in a model-based reconstruction scheme. The CNN parameters are learned in an end-to-end fashion by un-rolling the iterative algorithm. The main difference of the proposed scheme with current model-based deep learning strategies is the learning of non-linear annihilation relations in Fourier space using a modelbased framework. The experimental comparisons show that the proposed scheme can offer similar performance as SLR schemes in the calibrationless parallel MRI setting, while reducing the run-time by around three orders of magnitude. We also combine the proposed scheme with image domain priors, which are complementary, thus further improving the performance over SLR schemes.

The emerging field of precision oncology relies on the accurate pinpointing of alterations in the molecular profile of a tumor to provide personalized targeted treatments. Current methodologies in the field commonly include the application of next generation sequencing technologies to a tumor sample, followed by the identification of mutations in the DNA known as somatic variants. The differentiation of these variants from sequencing error poses a classic classification problem, which has traditionally been approached with Bayesian statistics, and more recently with supervised machine learning methods such as neural networks. Although these methods provide greater accuracy, classic neural networks lack the ability to indicate the confidence of a variant call. In this paper, we explore the performance of deep Bayesian neural networks on next generation sequencing data, and their ability to give probability estimates for somatic variant calls. In addition to demonstrating similar performance in comparison to standard neural networks, we show that the resultant output probabilities make these better suited to the disparate and highly-variable sequencing data-sets these models are likely to encounter in the real world. We aim to deliver algorithms to oncologists for which model certainty better reflects accuracy, for improved clinical application. By moving away from point estimates to reliable confidence intervals, we expect the resultant clinical and treatment decisions to be more robust and more informed by the underlying reality of the tumor molecular profile.

Deep neural networks have been demonstrated to be vulnerable to backdoor attacks. Specifically, by injecting a small number of maliciously constructed inputs into the training set, an adversary is able to plant a backdoor into the trained model. This backdoor can then be activated during inference by a backdoor trigger to fully control the model's behavior. While such attacks are very effective, they crucially rely on the adversary injecting arbitrary inputs that are---often blatantly---mislabeled. Such samples would raise suspicion upon human inspection, potentially revealing the attack. Thus, for backdoor attacks to remain undetected, it is crucial that they maintain label-consistency---the condition that injected inputs are consistent with their labels. In this work, we leverage adversarial perturbations and generative models to execute efficient, yet label-consistent, backdoor attacks. Our approach is based on injecting inputs that appear plausible, yet are hard to classify, hence causing the model to rely on the (easier-to-learn) backdoor trigger.

In real-world decision-making problems, for instance in the fields of finance, robotics or autonomous driving, keeping uncertainty under control is as important as maximizing expected returns. Risk aversion has been addressed in the reinforcement learning literature through risk measures related to the variance of returns. However, in many cases, the risk is measured not only on a long-term perspective, but also on the step-wise rewards (e.g., in trading, to ensure the stability of the investment bank, it is essential to monitor the risk of portfolio positions on a daily basis). In this paper, we define a novel measure of risk, which we call reward volatility, consisting of the variance of the rewards under the state-occupancy measure. We show that the reward volatility bounds the return variance so that reducing the former also constrains the latter. We derive a policy gradient theorem with a new objective function that exploits the mean-volatility relationship, and develop an actor-only algorithm. Furthermore, thanks to the linearity of the Bellman equations defined under the new objective function, it is possible to adapt the well-known policy gradient algorithms with monotonic improvement guarantees such as TRPO in a risk-averse manner. Finally, we test the proposed approach in two simulated financial environments.

Air quality is closely related to public health. Health issues such as cardiovascular diseases and respiratory diseases, may have connection with long exposure to highly polluted environment. Therefore, accurate air quality forecasts are extremely important to those who are vulnerable. To estimate the variation of several air pollution concentrations, previous researchers used various approaches, such as the Community Multiscale Air Quality model (CMAQ) or neural networks. Although CMAQ model considers a coverage of the historic air pollution data and meteorological variables, extra bias is introduced due to additional adjustment. In this paper, a combination of model-based strategy and data-driven method namely the physical-temporal collection(PTC) model is proposed, aiming to fix the systematic error that traditional models deliver. In the data-driven part, the first components are the temporal pattern and the weather pattern to measure important features that contribute to the prediction performance. The less relevant input variables will be removed to eliminate negative weights in network training. Then, we deploy a long-short-term-memory (LSTM) to fetch the preliminary results, which will be further corrected by a neural network (NN) involving the meteorological index as well as other pollutants concentrations. The data-set we applied for forecasting is from January 1st, 2016 to December 31st, 2016. According to the results, our PTC achieves an excellent performance compared with the baseline model (CMAQ prediction, GRU, DNN and etc.). This joint model-based data-driven method for air quality prediction can be easily deployed on stations without extra adjustment, providing results with high-time-resolution information for vulnerable members to prevent heavy air pollution ahead.

We are interested in the problem of continual object recognition in a setting which resembles that under which humans see and learn. This problem is of high relevance in all those applications where an agent must work collaboratively with a human in the same setting (e.g., personal assistance). The main innovative aspects of this setting with respect to the state-of-the-art are: it assumes an egocentric point-of-view bound to a single person, which implies a relatively low diversity of data and a cold start with no data; it requires to operate in a open world, where new objects can be encountered at any time; supervision is scarce and has to be solicited to the user, and completely unsupervised recognition of new objects should be possible. Note that this setting differs from the one addressed in the open world recognition literature, where supervised feedback is always requested to be able to incorporate new objects. We propose an incremental approach which is based on four main features: the use of time and space persistency (i.e., the appearance of objects changes relatively slowly), the use of similarity as the main driving principle for object recognition and novelty detection, the progressive introduction of new objects in a developmental fashion and the selective elicitation of user feedback in an online active learning fashion. Experimental results show the feasibility of open world, generic object recognition, the ability to recognize, memorize and re-identify new objects even in complete absence of user supervision, and the utility of persistency and incrementality in boosting performance.

Some veterans with a history of mild traumatic brain injury (mTBI) have reported experiencing auditory and visual dysfunction that persist beyond the acute phase of the incident. The etiology behind th ese symptoms is difficult to characterize, since mTBI is defined by negative imaging findings on current clinical imaging. There are several competing hypotheses that could explain functional deficits; one example is shear inju ry, which may manifest in dif fusion - weighted magnetic resonance (MR) imaging (DWI) . Herein, we explore this alternative hypothe sis in a pilot study of multi - parametric MR imaging. Briefly, we consider a cohort of 8 mTBI patients relative to 22 control subjects using structural T1 - weig hted imaging (T1w) and connectivity with DWI.