YOUR SEAT IS NOT GUARANTEED WITHOUT COMPLETING BOTH STEPS OF THE REGISTRATION PROCESS (MEETUP RSVP GOOGLE FORM) ABSTRACT The semiconductor industry is advancing in its journey of digital transformation, & digital twins are beginning to create the intelligence for optimizing engineering & operations throughout the semiconductor value chain. As virtual counterparts to the complex processes & assets that support product lifecycles, digital twins are deployed to help companies to attain higher levels of performance, greater efficiencies, & faster innovation cycles in semiconductor development. Enabled by cloud & edge technologies, pervasive data connectivity, artificial intelligence & data-augmented simulation, digital twins drive end-to-end engineering optimization & achieve deeper levels of integration both within & throughout design & operations. SPEAKERS Prith Banerjee, CTO, ANSYS [Moderator] Prith Banerjee is the Chief Technology Officer of ANSYS where he is responsible for leading the evolution of ANSYS Technology strategy & champion the companys next phase of innovation & growth He also serves on the Board of Directors of Cray & Cubic Corporation. Previously he used to be Senior Client Partner at Korn Ferry.

Many of our digital interactions are increasingly being done through artificial intelligence-driven chatbots & algorithms whether we realize it or not. As we enter this A.I. driven world we ask what are the design & ethical considerations as we enter this paradigm shift for humanity. Join us for this panel discussion as we ask our panelists: Just what is A.I. & what are the different types of A.I.? What's different about designing for A.I.? Where should you start learning to design for A.I.? What are the ethical considerations for designing for A.I.? Which A.I. related platform(s) should you learn? What jobs will open up for A.I.? Schedule 6:30 pm - Doors Open 7:00 pm - Panel Starts 8:30 pm - Panel Ends 9:00 pm - Event Ends / Doors Close Moderated by: Amy Stillhorn, Founder of Big Monocle Amy is the founder & CEO ofBig Monocle, an award-winning creative agency with offices in Utah & California (San Francisco, San Jose, Redwood City & Provo) that services startups & fortune 100 clients.

Just when we thought it was safe to go back in the water, a new threat has emerged to equal employment opportunity as employers base hiring decisions on artificial intelligence powered video and game-based "pre-employment" assessments of job candidates. The Electronic Privacy Information Center, a public interest research center based in Washington, D.C., recently asked the Federal Trade Commission to investigate HireVue, a recruiting company based in Utah that purports to evaluate a job applicant's job qualifications through online "video interview" and/or "game-based challenge." According to its web site, HireVue has more than 700 customers worldwide including over one-third of the Fortune 100 and such leading brands such as Unilever, Hilton, JP Morgan Chase, Delta Air Lines, Vodafone, Carnival Cruise Line, and Goldman Sachs. The company states it has hosted more than ten million on-demand interviews and one million assessments. The EPIC complaint follows a wave of lawsuits in recent years charging that employers are using software algorithms to discriminate against older workers by targeting internet job advertisements exclusively to younger workers.

Deep learning natural language processing models often use vector word embeddings, such as word2vec or GloVe, to represent words. A discrete sequence of words can be much more easily integrated with downstream neural layers if it is represented as a sequence of continuous vectors. Also, semantic relationships between words, learned from a text corpus, can be encoded in the relative configurations of the embedding vectors. However, storing and accessing embedding vectors for all words in a dictionary requires large amount of space, and may stain systems with limited GPU memory. Here, we used approaches inspired by quantum computing to propose two related methods, {\em word2ket} and {\em word2ketXS}, for storing word embedding matrix during training and inference in a highly efficient way. Our approach achieves a hundred-fold or more reduction in the space required to store the embeddings with almost no relative drop in accuracy in practical natural language processing tasks.

If an artificial intelligence aims to maximise risk-adjusted return, then under mild conditions it is disproportionately likely to pick an unethical strategy unless the objective function allows sufficiently for this risk. Even if the proportion ${\eta}$ of available unethical strategies is small, the probability ${p_U}$ of picking an unethical strategy can become large; indeed unless returns are fat-tailed ${p_U}$ tends to unity as the strategy space becomes large. We define an Unethical Odds Ratio Upsilon (${\Upsilon}$) that allows us to calculate ${p_U}$ from ${\eta}$, and we derive a simple formula for the limit of ${\Upsilon}$ as the strategy space becomes large. We give an algorithm for estimating ${\Upsilon}$ and ${p_U}$ in finite cases and discuss how to deal with infinite strategy spaces. We show how this principle can be used to help detect unethical strategies and to estimate ${\eta}$. Finally we sketch some policy implications of this work.

Endowing dialogue systems with personas is essential to deliver more human-like conversations. However, this problem is still far from well explored due to the difficulties of both embodying personalities in natural languages and the persona sparsity issue observed in most dialogue corpora. This paper proposes a pre-training based personalized dialogue model that can generate coherent responses using persona-sparse dialogue data. In this method, a pre-trained language model is used to initialize an encoder and decoder, and personal attribute embeddings are devised to model richer dialogue contexts by encoding speakers' personas together with dialogue histories. Further, to incorporate the target persona in the decoding process and to balance its contribution, an attention routing structure is devised in the decoder to merge features extracted from the target persona and dialogue contexts using dynamically predicted weights. Our model can utilize persona-sparse dialogues in a unified manner during the training process, and can also control the amount of persona-related features to exhibit during the inference process. Both automatic and manual evaluation demonstrates that the proposed model outperforms state-of-the-art methods for generating more coherent and persona consistent responses with persona-sparse data.

This paper presents aplib, a Java library for programming intelligent agents, featuring BDI and multi agency, but adding on top of it a novel layer of tactical programming inspired by the domain of theorem proving. Aplib is also implemented in such a way to provide the fluency of a Domain Specific Language (DSL). Compared to dedicated BDI agent programming languages such as JASON, 2APL, or GOAL,aplib's embedded DSL approach does mean that \aplib\ programmers will still be limited by Java syntax, but on other hand they get all the advantages that Java programmers get: rich language features (object orientation, static type checking, $\lambda$-expression, libraries, etc), a whole array of development tools, integration with other technologies, large community, etc.

Genetic programming (GP) is a general purpose bio-inspired meta-heuristic for the evolution of computer programs. In contrast to the several successful applications, there is little understanding of the working principles behind GP. In this paper we present a performance analysis that sheds light on the behaviour of simple GP systems for evolving conjunctions of n variables (ANDn). The analysis of a random local search GP system with minimal terminal and function sets reveals the relationship between the number of iterations and the progress the GP makes toward finding the target function. Afterwards we consider a more realistic GP system equipped with a global mutation operator and prove that it can efficiently solve ANDn by producing programs of linear size that fit a training set to optimality and with high probability generalise well. Additionally, we consider more general problems which extend the terminal set with undesired variables or negated variables. In the presence of undesired variables, we prove that, if non-strict selection is used, then the algorithm fits the complete training set efficiently while the strict selection algorithm may fail with high probability unless the substitution operator is switched off. If negations are allowed, we show that while the algorithms fail to fit the complete training set, the constructed solutions generalise well. Finally, from a problem hardness perspective, we reveal the existence of small training sets that allow the evolution of the exact conjunctions even with access to negations or undesired variables.

We introduce two tactics to attack agents trained by deep reinforcement learning algorithms using adversarial examples, namely the strategically-timed attack and the enchanting attack. In the strategically-timed attack, the adversary aims at minimizing the agent's reward by only attacking the agent at a small subset of time steps in an episode. Limiting the attack activity to this subset helps prevent detection of the attack by the agent. We propose a novel method to determine when an adversarial example should be crafted and applied. In the enchanting attack, the adversary aims at luring the agent to a designated target state. This is achieved by combining a generative model and a planning algorithm: while the generative model predicts the future states, the planning algorithm generates a preferred sequence of actions for luring the agent. A sequence of adversarial examples is then crafted to lure the agent to take the preferred sequence of actions. We apply the two tactics to the agents trained by the state-of-the-art deep reinforcement learning algorithm including DQN and A3C. In 5 Atari games, our strategically timed attack reduces as much reward as the uniform attack (i.e., attacking at every time step) does by attacking the agent 4 times less often. Our enchanting attack lures the agent toward designated target states with a more than 70% success rate. Videos are available at http://yenchenlin.me/adversarial_attack_RL/

Convolutional neural networks (CNNs) have been widely and successfully used for medical image segmentation. However, CNNs are typically considered to require large numbers of dedicated expert-segmented training volumes, which may be limiting in practice. This work investigates whether clinically obtained segmentations which are readily available in picture archiving and communication systems (PACS) could provide a possible source of data to train a CNN for segmentation of organs-at-risk (OARs) in radiotherapy treatment planning. In such data, delineations of structures deemed irrelevant to the target clinical use may be lacking. To overcome this issue, we use multi-label instead of multi-class segmentation. We empirically assess how many clinical delineations would be sufficient to train a CNN for the segmentation of OARs and find that increasing the training set size beyond a limited number of images leads to sharply diminishing returns. Moreover, we find that by using multi-label segmentation, missing structures in the reference standard do not have a negative effect on overall segmentation accuracy. These results indicate that segmentations obtained in a clinical workflow can be used to train an accurate OAR segmentation model.