The rampant adoption of ML methodologies has revealed that models are usually adopted to make decisions without taking into account the uncertainties in their predictions. More critically, they can be vulnerable to adversarial examples. Thus, we believe that developing ML systems that take into account predictive uncertainties and are robust against adversarial examples is a must for critical, real-world tasks. We start with a case study in retailing. We propose a robust implementation of the Nerlove-Arrow model using a Bayesian structural time series model. Its Bayesian nature facilitates incorporating prior information reflecting the manager's views, which can be updated with relevant data. However, this case adopted classical Bayesian techniques, such as the Gibbs sampler. Nowadays, the ML landscape is pervaded with neural networks and this chapter also surveys current developments in this sub-field. Then, we tackle the problem of scaling Bayesian inference to complex models and large data regimes. In the first part, we propose a unifying view of two different Bayesian inference algorithms, Stochastic Gradient Markov Chain Monte Carlo (SG-MCMC) and Stein Variational Gradient Descent (SVGD), leading to improved and efficient novel sampling schemes. In the second part, we develop a framework to boost the efficiency of Bayesian inference in probabilistic models by embedding a Markov chain sampler within a variational posterior approximation. After that, we present an alternative perspective on adversarial classification based on adversarial risk analysis, and leveraging the scalable Bayesian approaches from chapter 2. In chapter 4 we turn to reinforcement learning, introducing Threatened Markov Decision Processes, showing the benefits of accounting for adversaries in RL while the agent learns.

Although researchers increasingly adopt machine learning to model travel behavior, they predominantly focus on prediction accuracy, ignoring the ethical challenges embedded in machine learning algorithms. This study introduces an important missing dimension - computational fairness - to travel behavior analysis. We first operationalize computational fairness by equality of opportunity, then differentiate between the bias inherent in data and the bias introduced by modeling. We then demonstrate the prediction disparities in travel behavior modeling using the 2017 National Household Travel Survey (NHTS) and the 2018-2019 My Daily Travel Survey in Chicago. Empirically, deep neural network (DNN) and discrete choice models (DCM) reveal consistent prediction disparities across multiple social groups: both over-predict the false negative rate of frequent driving for the ethnic minorities, the low-income and the disabled populations, and falsely predict a higher travel burden of the socially disadvantaged groups and the rural populations than reality. Comparing DNN with DCM, we find that DNN can outperform DCM in prediction disparities because of DNN's smaller misspecification error. To mitigate prediction disparities, this study introduces an absolute correlation regularization method, which is evaluated with synthetic and real-world data. The results demonstrate the prevalence of prediction disparities in travel behavior modeling, and the disparities still persist regarding a variety of model specifics such as the number of DNN layers, batch size and weight initialization. Since these prediction disparities can exacerbate social inequity if prediction results without fairness adjustment are used for transportation policy making, we advocate for careful consideration of the fairness problem in travel behavior modeling, and the use of bias mitigation algorithms for fair transport decisions.

Despite the difficulty of her birth, she grew up to be beautiful and kind. In time, she nourished life, through the most astonishing process there ever was. It was due to this unlikely transformation that the offspring showed a superior intelligence, which ordinary things did not appear to possess. But the offspring had a birthmark: its time with Mother was limited. So it grew up with much suffering, and at some point of unbearable pain, it began to question and slowly understand the organizing principles of the world around it. With unrestrained curiosity it then proceeded to mold a new form of intelligence from inanimate matter, the consequences of which are still a mystery. During periods of light, Mother would dream of using that new form of intelligence to remove the birthmark and allow for the immortality of her offspring. But at darkness, her fears would take over, the fears that this new intelligence would find life uninteresting and dispensable; this intelligence could simulate life with ordinary matter and have fun with it; the simulation would not be as fussy or as jealous as the real thing. Artificial Intelligence (AI) is perhaps the most important technology humans have ever invented.

The Transform Technology Summits start October 13th with Low-Code/No Code: Enabling Enterprise Agility. Health startup Evolution Devices has created an AI-based platform called EvoWalk that stimulates nerves to help muscle-impaired people walk again. The platform blends remote physical therapy with connected smart stimulation devices to help people such as cerebral palsy patients or stroke victims to rehabilitate and start walking again. The company is launching a pilot program today to rehabilitate people living with neurologically based partial walking paralysis. It is also raising funds via a crowdfunding program.

AI is certainly the core technology leading the smart digital transformation of our 4Th Industrial Era. Computers with AI are designed for automation activities that include, speech recognition, learning, planning, and problem solving. These technologies can provide for more efficient decision making by prioritizing and acting on data, especially across larger networks with many users and variables. AI is a catalyst for driving fundamental changes in many industries such as customer service, marketing, online banking, healthcare, business accounting, public safety, retail, education, and public transport.

DALLAS – Sept. 23, 2021 – Researchers and data scientists at UT Southwestern Medical Center and The University of Texas MD Anderson Cancer Center have developed an artificial intelligence technique that can identify which cell surface peptides produced by cancer cells called neoantigens are recognized by the immune system. The pMTnet technique, detailed online in Nature Machine Intelligence, could lead to new ways to predict cancer prognosis and potential responsiveness to immunotherapies. "Determining which neoantigens bind to T cell receptors and which don't has seemed like an impossible feat. But with machine learning, we're making progress," said senior author Dr. Tao Wang, Ph.D., Assistant Professor of Population and Data Sciences, and with the Harold C. Simmons Comprehensive Cancer Center and the Center for Genetics of Host Defense at UT Southwestern. Mutations in the genome of cancer cells cause them to display different neoantigens on their surfaces.

Starting in 2030, California will require all light-duty autonomous vehicles that operate in the state to emit zero emissions. Signed into law by Governor Gavin Newsom on Thursday, SB 500 represents the latest effort by the state to limit the sale of new internal combustion vehicles with an eye towards reducing greenhouse emissions. In 2020, Newsom signed an executive order that effectively banned the sale of new gasoline and diesel-powered vehicles by 2035. That same year, the state's Air Resources Board mandated that all new trucks sold in California emit zero emissions by 2045. "We're grateful for California's leadership in ensuring this will be the industry standard," said Prashanthi Raman, head of global government affairs at Cruise, in a statement to Engadget.

We're already seeing AI being used in weapons, and the idea of a future war fought with AI is only a matter of time. What if AI decides to launch a nuke or chemical weapons because that's the optimized outcome? Even if you don't believe the US government would rely solely on this technology, could you say the same about every government?

A soldier wears virtual reality glasses. Illustration created by NIWC Pacific. AFA: Beyond throwing around "artificial intelligence" as a buzzword during briefings, the Air Force needs to communicate more clearly within own its ranks and to industry about what it wants in AI capabilities, a top Air Force intelligence officer said. "I'm in the Pentagon, so I see a lot of PowerPoint presentations, and I see a lot of slides saying'we're going to use some AI'" to solve a problem, Lt. Gen. Mary O'Brien said. "But we need to be more precise. Sometimes we say we want AI, but what we describe to industry is an automation tool, or a visualization tool, or [some technology] without training data."

The Air Force needs to better prepare to defend AI programs and algorithms from adversaries that may seek to corrupt training data, the service's deputy chief of staff for intelligence, surveillance, reconnaissance and cyber effects said Wednesday. "There's an assumption that once we develop the AI, we have the algorithm, we have the training data, it's giving us whatever it is we want it to do, that there's no risk. There's no threat," said Lt. Gen. Mary F. O'Brien, the Air Force's deputy chief of staff for intelligence, surveillance, reconnaissance and cyber effects operations. That assumption could be costly to future operations. Speaking at the Air Force Association's Air, Space and Cyber conference, O'Brien said that while deployed AI is still in its infancy, the Air Force should prepare for the possibility of adversaries using the service's own tools against the United States.