In machine learning, the vanishing gradient problem is the problem of greatly diverging gradient magnitudes between earlier and later layers encountered when training neural networks with backpropagation. In such methods, neural network weights are updated proportional to their partial derivative of the loss function. As the number of forward propagation steps in a network increases, for instance due to greater network depth, the gradients of earlier weights are calculated with increasingly many multiplications. These multiplications shrink the gradient magnitude. Consequently, the gradients of earlier weights will be exponentially smaller than the gradients of later weights. This difference in gradient magnitude might introduce instability in the training process, slow it, or halt it entirely. For instance, consider the hyperbolic tangent activation function. The gradients of this function are in range [0,1]. The product of repeated multiplication with such gradients decreases exponentially. The inverse problem, when weight gradients at earlier layers get exponentially larger, is called the exploding gradient problem. Backpropagation allowed researchers to train supervised deep artificial neural networks from scratch, initially with little success. Hochreiter's diplom thesis of 1991 formally identified the reason for this failure in the "vanishing gradient problem", which not only affects many-layered feedforward networks, but also recurrent networks. The latter are trained by unfolding them into very deep feedforward networks, where a new layer is created for each time-step of an input sequence processed by the network (the combination of unfolding and backpropagation is termed backpropagation through time). == Prototypical models == This section is based on the paper On the difficulty of training Recurrent Neural Networks by Pascanu, Mikolov, and Bengio. === Recurrent network model === A generic recurrent network has hidden states h 1 , h 2 , … {\displaystyle h_{1},h_{2},\dots } , inputs u 1 , u 2 , … {\displaystyle u_{1},u_{2},\dots } , and outputs x 1 , x 2 , … {\displaystyle x_{1},x_{2},\dots } . Let it be parameterized by θ {\displaystyle \theta } , so that the system evolves as ( h t , x t ) = F ( h t − 1 , u t , θ ) {\displaystyle (h_{t},x_{t})=F(h_{t-1},u_{t},\theta )} Often, the output x t {\displaystyle x_{t}} is a function of h t {\displaystyle h_{t}} , as some x t = G ( h t ) {\displaystyle x_{t}=G(h_{t})} . The vanishing gradient problem already presents itself clearly when x t = h t {\displaystyle x_{t}=h_{t}} , so we simplify our notation to the special case with: x t = F ( x t − 1 , u t , θ ) {\displaystyle x_{t}=F(x_{t-1},u_{t},\theta )} Now, take its differential: d x t = ∇ θ F ( x t − 1 , u t , θ ) d θ + ∇ x F ( x t − 1 , u t , θ ) d x t − 1 = ∇ θ F ( x t − 1 , u t , θ ) d θ + ∇ x F ( x t − 1 , u t , θ ) [ ∇ θ F ( x t − 2 , u t − 1 , θ ) d θ + ∇ x F ( x t − 2 , u t − 1 , θ ) d x t − 2 ] ⋮ = [ ∇ θ F ( x t − 1 , u t , θ ) + ∇ x F ( x t − 1 , u t , θ ) ∇ θ F ( x t − 2 , u t − 1 , θ ) + ⋯ ] d θ {\displaystyle {\begin{aligned}dx_{t}&=\nabla _{\theta }F(x_{t-1},u_{t},\theta )d\theta +\nabla _{x}F(x_{t-1},u_{t},\theta )dx_{t-1}\\&=\nabla _{\theta }F(x_{t-1},u_{t},\theta )d\theta +\nabla _{x}F(x_{t-1},u_{t},\theta )\left[\nabla _{\theta }F(x_{t-2},u_{t-1},\theta )d\theta +\nabla _{x}F(x_{t-2},u_{t-1},\theta )dx_{t-2}\right]\\&\;\;\vdots \\&=\left[\nabla _{\theta }F(x_{t-1},u_{t},\theta )+\nabla _{x}F(x_{t-1},u_{t},\theta )\nabla _{\theta }F(x_{t-2},u_{t-1},\theta )+\cdots \right]d\theta \end{aligned}}} Training the network requires us to define a loss function to be minimized. Let it be L ( x T , u 1 , … , u T ) {\displaystyle L(x_{T},u_{1},\dots ,u_{T})} , then minimizing it by gradient descent gives Δ θ = − η ⋅ [ ∇ x L ( x T ) ( ∇ θ F ( x t − 1 , u t , θ ) + ∇ x F ( x t − 1 , u t , θ ) ∇ θ F ( x t − 2 , u t − 1 , θ ) + ⋯ ) ] T {\displaystyle \Delta \theta =-\eta \cdot \left[\nabla _{x}L(x_{T})\left(\nabla _{\theta }F(x_{t-1},u_{t},\theta )+\nabla _{x}F(x_{t-1},u_{t},\theta )\nabla _{\theta }F(x_{t-2},u_{t-1},\theta )+\cdots \right)\right]^{T}} where η {\displaystyle \eta } is the learning rate. The vanishing/exploding gradient problem appears because there are repeated multiplications, of the form ∇ x F ( x t − 1 , u t , θ ) ∇ x F ( x t − 2 , u t − 1 , θ ) ∇ x F ( x t − 3 , u t − 2 , θ ) ⋯ {\displaystyle \nabla _{x}F(x_{t-1},u_{t},\theta )\nabla _{x}F(x_{t-2},u_{t-1},\theta )\nabla _{x}F(x_{t-3},u_{t-2},\theta )\cdots } ==== Example: recurrent network with sigmoid activation ==== For a concrete example, consider a typical recurrent network defined by x t = F ( x t − 1 , u t , θ ) = W rec σ ( x t − 1 ) + W in u t + b {\displaystyle x_{t}=F(x_{t-1},u_{t},\theta )=W_{\text{rec}}\sigma (x_{t-1})+W_{\text{in}}u_{t}+b} where θ = ( W rec , W in ) {\displaystyle \theta =(W_{\text{rec}},W_{\text{in}})} is the network parameter, σ {\displaystyle \sigma } is the sigmoid activation function, applied to each vector coordinate separately, and b {\displaystyle b} is the bias vector. Then, ∇ x F ( x t − 1 , u t , θ ) = W rec diag ( σ ′ ( x t − 1 ) ) {\displaystyle \nabla _{x}F(x_{t-1},u_{t},\theta )=W_{\text{rec}}\operatorname {diag} (\sigma '(x_{t-1}))} , and so ∇ x F ( x t − 1 , u t , θ ) ∇ x F ( x t − 2 , u t − 1 , θ ) ⋯ ∇ x F ( x t − k , u t − k + 1 , θ ) = W rec diag ( σ ′ ( x t − 1 ) ) W rec diag ( σ ′ ( x t − 2 ) ) ⋯ W rec diag ( σ ′ ( x t − k ) ) {\displaystyle {\begin{aligned}&\nabla _{x}F(x_{t-1},u_{t},\theta )\nabla _{x}F(x_{t-2},u_{t-1},\theta )\cdots \nabla _{x}F(x_{t-k},u_{t-k+1},\theta )\\&=W_{\text{rec}}\operatorname {diag} (\sigma '(x_{t-1}))W_{\text{rec}}\operatorname {diag} (\sigma '(x_{t-2}))\cdots W_{\text{rec}}\operatorname {diag} (\sigma '(x_{t-k}))\end{aligned}}} Since | σ ′ | ≤ 1 {\displaystyle \left|\sigma '\right|\leq 1} , the operator norm of the above multiplication is bounded above by ‖ W rec ‖ k {\displaystyle \left\|W_{\text{rec}}\right\|^{k}} . So if the spectral radius of W rec {\displaystyle W_{\text{rec}}} is γ < 1 {\displaystyle \gamma <1} , then at large k {\displaystyle k} , the above multiplication has operator norm bounded above by γ k → 0 {\displaystyle \gamma ^{k}\to 0} . This is the prototypical vanishing gradient problem. The effect of a vanishing gradient is that the network cannot learn long-range effects. Recall Equation (loss differential): ∇ θ L = ∇ x L ( x T , u 1 , … , u T ) [ ∇ θ F ( x t − 1 , u t , θ ) + ∇ x F ( x t − 1 , u t , θ ) ∇ θ F ( x t − 2 , u t − 1 , θ ) + ⋯ ] {\displaystyle \nabla _{\theta }L=\nabla _{x}L(x_{T},u_{1},\dots ,u_{T})\left[\nabla _{\theta }F(x_{t-1},u_{t},\theta )+\nabla _{x}F(x_{t-1},u_{t},\theta )\nabla _{\theta }F(x_{t-2},u_{t-1},\theta )+\cdots \right]} The components of ∇ θ F ( x , u , θ ) {\displaystyle \nabla _{\theta }F(x,u,\theta )} are just components of σ ( x ) {\displaystyle \sigma (x)} and u {\displaystyle u} , so if u t , u t − 1 , … {\displaystyle u_{t},u_{t-1},\dots } are bounded, then ‖ ∇ θ F ( x t − k − 1 , u t − k , θ ) ‖ {\displaystyle \left\|\nabla _{\theta }F(x_{t-k-1},u_{t-k},\theta )\right\|} is also bounded by some M > 0 {\displaystyle M>0} , and so the terms in ∇ θ L {\displaystyle \nabla _{\theta }L} decay as M γ k {\displaystyle M\gamma ^{k}} . This means that, effectively, ∇ θ L {\displaystyle \nabla _{\theta }L} is affected only by the first O ( γ − 1 ) {\displaystyle O(\gamma ^{-1})} terms in the sum. If γ ≥ 1 {\displaystyle \gamma \geq 1} , the above analysis does not quite work. For the prototypical exploding gradient problem, the next model is clearer. === Dynamical systems model === Following (Doya, 1993), consider this one-neuron recurrent network with sigmoid activation: x t + 1 = ( 1 − ε ) x t + ε σ ( w x t + b ) + ε w ′ u t {\displaystyle x_{t+1}=(1-\varepsilon )x_{t}+\varepsilon \sigma (wx_{t}+b)+\varepsilon w'u_{t}} At the small ε {\displaystyle \varepsilon } limit, the dynamics of the network becomes d x d t = − x ( t ) + σ ( w x ( t ) + b ) + w ′ u ( t ) {\displaystyle {\frac {dx}{dt}}=-x(t)+\sigma (wx(t)+b)+w'u(t)} Consider first the autonomous case, with u = 0 {\displaystyle u=0} . Set w = 5.0 {\displaystyle w=5.0} , and vary b {\displaystyle b} in [ − 3 , − 2 ] {\displaystyle [-3,-2]} . As b {\displaystyle b} decreases, the system has 1 stable point, then has 2 stable points and 1 unstable point, and finally has 1 stable point again. Explicitly, the stable points are ( x , b ) = ( x , ln ( x 1 − x ) − 5 x ) {\displaystyle (x,b)=\left(x,\ln \left({\frac {x}{1-x}}\right)-5x\right)} . Now consider Δ x ( T ) Δ x ( 0 ) {\displaystyle {\frac {\Delta x(T)}{\Delta x(0)}}} and Δ x ( T ) Δ b {\displaystyle {\frac {\Delta x(T)}{\Delta b}}} , where T {\displaystyle T} is large enough that the system has settled into one of the stable points. If ( x ( 0 ) , b ) {\displaystyle (x(0),b)} puts the system very close to an unstable point, then a tiny variation in x ( 0 ) {\displaystyle x(0)} or b {\displaystyle b} wo
Act! LLC
ACT! (previously known as Activity Control Technology, Automated Contact Tracking, ACT! by Sage, and Sage ACT!) is a customer relationship management and marketing automation software platform designed for small and medium-sized businesses. It has over 2.8 million registered users as of December 2014. == History == The company Conductor Software was founded in 1986, in Dallas, Texas, by Pat Sullivan and Mike Muhney. The original name for the software was Activity Control Technology; it was renamed to Automated Contact Tracking, later abbreviated to ACT. The name of the company was subsequently changed to Contact Software International and it was sold in 1993 to Symantec Corporation, who in 1999 then sold it to SalesLogix. The Sage Group purchased Interact Commerce (formerly SalesLogix) in 2001 through Best Software, then its North American software division. Swiftpage acquired it in 2013. Beginning with the 2006 version, the name was styled ACT! by Sage, and in 2010 revised to Sage ACT!. Following its 2013 acquisition by Swiftpage, it was renamed to ACT! Swiftpage. In May 2018, ACT! was sold to SFW Advisors. In December 2018, Kuvana, a marketing automation software solution, was acquired by SFW and merged with ACT! This add-on is now a complementary service to the core CRM solution. In December 2019, ACT! hired Steve Oriola as chairman and CEO. In 2020, Swiftpage changed its company name to ACT!. In March 2023, ACT! hired Bruce Reading as President and CEO. == Software == ACT! features include contact, company and opportunity management, a calendar, marketing automation and e-marketing tools, reports, interactive dashboards with graphical visualizations, and the ability to track prospective customers. ACT! integrates with Microsoft Word, Excel, Outlook, Google Contacts, Gmail, and other applications via Zapier. For custom integrations, ACT! has an in-built API. ACT! can be accessed from Windows desktops (Win7 and later) with local or network shared database; synchronized to laptops or remote officers; Citrix or Remote Desktop; Web browsers (Premium only) with self or SaaS hosting; smartphones and tablets via HTML5 Web (Premium only); smartphones and tablets via sync with Handheld Contact.
The Best Free AI Humanizer for Beginners
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The Best Free AI Image Generator for Beginners
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Yasuo Matsuyama
Yasuo Matsuyama (born March 23, 1947) is a Japanese researcher in machine learning and human-aware information processing. Matsuyama is a Professor Emeritus and an Honorary Researcher of the Research Institute of Science and Engineering of Waseda University. == Early life and education == Matsuyama received his bachelor’s, master’s and doctoral degrees in electrical engineering from Waseda University in 1969, 1971, and 1974 respectively. The dissertation title for the Doctor of Engineering is Studies on Stochastic Modeling of Neurons. There, he contributed to the spiking neurons with stochastic pulse-frequency modulation. Advisors were Jun’ichi Takagi, Kageo, Akizuki, and Katsuhiko Shirai. Upon the completion of the doctoral work at Waseda University, he was dispatched to the United States as a Japan-U.S. exchange fellow by the joint program of the Japan Society for the Promotion of Science, Fulbright Program, and the Institute of International Education. Through this exchange program, he completed his Ph.D. program at Stanford University in 1978. The dissertation title is Process Distortion Measures and Signal Processing. There, he contributed to the theory of probabilistic distortion measures and its applications to speech encoding with spectral clustering or vector quantization. His advisor was Robert. M. Gray. == Career == From 1977 to 1078, Matsuyama was a research assistant at the Information Systems Laboratory of Stanford University Archived 2018-03-16 at the Wayback Machine. From 1979 to 1996, he was a faculty of Ibaraki University, Japan (the final position was a professor and chairperson of the Information and System Sciences Major). Since 1996, he was a Professor of Waseda University, Department of Computer Science and Engineering. From 2011 to 2013, he was the director of the Media Network Center of Waseda University. At the 2011 Tōhoku earthquake and tsunami of March 11, 2011, he was in charge of the safety inquiry of 65,000 students, staffs and faculties. Since 2017, Matsuyama is a Professor Emeritus and an Honorary Researcher of the Research Institute of Science and Engineering of Waseda University. Since 2018, he serves as an acting president of the Waseda Electrical Engineering Society. == Work == Matsuyama’s works on machine learning and human-aware information processing have dual foundations. Studies on the competitive learning (vector quantization) for his Ph.D. at Stanford University brought about his succeeding works on machine learning contributions. Studies on stochastic spiking neurons for his Dr. Engineering at Waseda University set off applications of biological signals to the machine learning. Thus, his works can be grouped reflecting these dual foundations. Statistical machine learning algorithms: The use of the alpha-logarithmic likelihood ratio in learning cycles generated the alpha-EM algorithm (alpha-Expectation maximization algorithm). Because the alpha-logarithm includes the usual logarithm, the alpha-EM algorithm contains the EM-algorithm (more precisely, the log-EM algorithm). The merit of the speedup by the alpha-EM over the log-EM is due to the ability to utilize the past information. Such a usage of the messages from the past brought about the alpha-HMM estimation algorithm (alpha-hidden Markov model estimation algorithm) that is a generalized and faster version of the hidden Markov model estimation algorithm (HMM estimation algorithm). Competitive learning on empirical data: Starting from the speech compression studies at Stanford, Matsuyama developed generalized competitive learning algorithms; the harmonic competition and the multiple descent cost competition. The former realizes the multiple-object optimization. The latter admits deformable centroids. Both algorithms generalize the batch-mode vector quantization (simply called, vector quantization) and the successive-mode vector quantization (or, called learning vector quantization). A hierarchy from the alpha-EM to the vector quantization: Matsuyama contributed to generate and identify the hierarchy of the above algorithms. Alpha-EM ⊃ log-EM ⊃ basic competitive learning (vector quantization, VQ; or clustering). On the class of the vector quantization and competitive learning, he contributed to generate and identify the hierarchy of VQs. VQ ⇔ {batch mode VQ, and learning VQ} ⊂ {harmonic competition} ⊂ {multiple descent cost competition}. Applications to Human-aware information processing: The dual foundations of his led to the applications to huma-aware information processing. Retrieval systems for similar images and videos. Bipedal humanoid operations via invasive and noninvasive brain signals as well as gestures. Continuous authentication of uses by brain signals. Self-organization and emotional feature injection based on the competitive learning. Decomposition of DNA sequences by the independent component analysis (US Patent: US 8,244,474 B2). Data compression of speech signals by the competitive learning. The above theories and applications work as contributions to IoCT (Internet of Collaborative Things) and IoXT (http://www.asc-events.org/ASC17/Workshop.php Archived 2018-02-06 at the Wayback Machine). == Awards and honors == 2016: e-Teaching Award of Waseda University 2015: Best Textbook Award by the Japanese Society of Information Processing 2014: Fellow of the Japanese Society of Information Processing 2013: IEEE Life Fellow 2008: Y. Dote Memorial Best Paper Award of CSTST 2008 from ACM and IEEE 2006: LSI Intellectual Property Design Award from the LSI IP Committee 2004: Best Paper Award for Application Oriented Research from Asia Pacific Neural Network Assembly 2002: Fellow Award from the Institute of Electronics, Information and Communication Engineers. 2001: Telecommunication System Major Award of the Telecommunications Advancement Foundation 2001: Outstanding Paper Award of IEEE Transactions on Neural Networks Archived 2013-01-17 at the Wayback Machine 1998: Fellow Award from IEEE for contributions to learning algorithms with competition. 1992: Best Paper Award from the Institute of Electronics, Information and Communication Engineers 1989: Telecommunication System Promotion Award of the Telecommunications Advancement Foundation
CloudHealth Technologies
CloudHealth Technologies, now CloudHealth by VMware, is a software company based in Boston, Massachusetts. The company provides cloud computing services related to cost management, governance, automation, security, and performance. == History == CloudHealth Technologies was founded by Joe Kinsella in 2012. Dan Phillips joined as CEO and co-founder in late 2012, and Dave Eicher joined as co-Founder in January 2013. In May 2016, the company announced plans to expand from its Boston headquarters with branch offices in San Francisco, London, Washington, D.C., Sydney, Amsterdam, Tel Aviv, and Singapore. Headquarters moved in Boston from Fort Point to 100 Summer Street in the Spring of 2018, tripling in square footage. In September 2017, Tom Axbey—who was previously at Rave Mobile Safety—joined as the new CEO and President. VMware announced its intention to acquire CloudHealth Technologies on August 27, 2018. The acquisition is "part of the information technology company's continued push into cloud-based software services" according to Reuters. The deal closed on October 4, 2018, and was reported to be in excess of $500 million. == Technology == Delivered through a software as a service (SaaS) model, CloudHealth Technologies's platform collects and analyzes data from cloud computing services and other IT environments so clients can report on costs, inform their business models, and project future trends. CloudHealth Technologies is compatible with Amazon Web Services, Microsoft Azure, Google Cloud Platform, multicloud, and hybrid cloud environments. CloudHealth Technologies has received Amazon Web Services(AWS) Education Competency status, AWS Migration Competency status and achieved SOC 2 Type 2 Compliance. == Funding == As of June 2017, CloudHealth Technologies has raised a total of $85.7 million through four rounds of funding. In March 2013, CloudHealth Technologies announced that it had secured $4.5 million in Series A funding. This round was led by .406 Ventures and Sigma Prime Ventures. In January 2015, CloudHealth Technologies secured $12 million in Series B funding. This round was led by Scale Venture Partners, .406 Ventures, and Sigma Prime Ventures, and was followed by a $3.2 million extension round. In May 2016, CloudHealth Technologies announced $20 million in Series C funding, led by Sapphire Ventures, .406 Ventures, Scale Venture Partners and Sigma Prime Ventures. In June 2017, CloudHealth Technologies secured $46 million in Series D funding led by Kleiner Perkins Caufield & Byers with participation from Meritech Capital Partners, Sapphire Ventures, 406 Ventures, and Scale Venture Partners. == Competition == As of March 2023, CloudHealth Technologies competes with Cloudability by Apptio and CloudCheckr by NetApp.
Oren Etzioni
Oren Etzioni (born 1964) is Professor Emeritus of Computer Science at the University of Washington, and founding CEO of the Allen Institute for Artificial Intelligence (AI2). Etzioni is a co-founder of Vercept, an AI startup, and founder and CEO of TrueMedia.org, a non-profit dedicated to fighting political deepfakes, which launched in April 2024. He is also the Founder and Technical Director of the AI2 Incubator and a venture partner at the Madrona Venture Group. == Early life and education == Etzioni is the son of Israeli-American intellectual Amitai Etzioni. He was the first student to major in computer science at Harvard University, where he earned a bachelor's degree in 1986. He earned a PhD from Carnegie Mellon University in January, 1991, supervised by Tom M. Mitchell. == University of Washington career == Etzioni joined the University of Washington faculty in 1991, immediately after receiving his PhD. He rose through the ranks to become the Washington Research Foundation Entrepreneurship Professor in Computer Science & Engineering. Etzioni's research has been focused on basic problems in the study of intelligence, machine reading, machine learning and web search. Past projects include Internet Softbots—the study of intelligent agents in the context of real-world software testbeds. In 2003, he started the KnowItAll project for acquiring massive amounts of information from the web. In 2005, he founded and became the director of the university's Turing Center. The center investigated problems in data mining, natural language processing, the Semantic Web and other web search topics. Etzioni coined the term machine reading and helped to create the first commercial comparison shopping agent. He has published over 200 technical papers, and his H-index exceeds 100. == Entrepreneurship == As a faculty member Etzioni was also an active entrepreneur, founding multiple companies and pioneering multiple technologies including MetaCrawler (bought by Infospace), Netbot (bought by Excite in 1997 for $35 million), and ClearForest (bought by Reuters). He founded Farecast, a travel metasearch and price prediction site, which was acquired by Microsoft in 2008 for $115 million. Before founding Farecast, he developed a program originally called Hamlet, that used algorithms to identify patterns in airfare data using data-mining techniques. He also co-founded Decide.com, a website to help consumers make buying decisions using previous price history and recommendations from other users. Decide.com was bought by eBay in September, 2013. Etzioni is also a venture partner at the Madrona Venture Group. He is founder and CEO of TrueMedia.org, a non-profit dedicated to fighting political deepfakes, which launched in April 2024. Etzioni is a co-founder of Vercept, an AI startup formed in 2025. == Founding CEO of AI2 == In September 2013 Etzioni was selected as the Founding CEO of the Allen Institute for Artificial Intelligence by philanthropist Paul G. Allen, and in January 2014 he took a leave of absence from the University of Washington to serve in that role. Etzioni's technical contributions continued at AI2; for example, in 2015, he helped to create the Semantic Scholar search engine. Under Etzioni’s leadership, AI2 grew from zero to over two hundred team members including notable researchers and engineers across several domains of AI. By 2021, its AI2 researchers had published near 700 papers in publications such as AAAI, ACL, CVPR, NeurIPS, and ICLR. Twenty-four of these papers had garnered special-recognition awards. AI2 also offered several key resources and tools to the AI community including the AllenNLP library, Semantic Scholar, and the conservation platforms EarthRanger and Skylight. Ed Lazowska, AI2 Board Member, has stated about Etzioni that he "took the collegial, collaborative culture that he absorbed in his 20+ years as a professor in UW's Allen School and mixed it with the singular focus that drives startups to create an elixir that AI2 folks have been drinking over the last eight years. The result is an exceptional organization of scientists, engineers, and entrepreneurs that's pursuing Paul Allen’s vision of ‘AI for the Common Good’ with extraordinary success.” == Popular press == In addition to his scientific publications, Etzioni has written commentary on AI for The New York Times, Wired, Nature, and other publications. After reading the idea in a book about AI by Brad Smith and Harry Shum, Etzioni has attempted to create an oath for AI practitioners. In 2018, he published what he called a "Hippocratic Oath for artificial intelligence practitioners" in TechCrunch. == Awards and recognition == In 1993, Etzioni received a National Young Investigator Award. In 2003, Etzioni was elected as AAAI Fellow. In 2005, Etzioni received an IJCAI Distinguished Paper Award for "A Probabilistic Model of Redundancy in Information Extraction". In 2007, he received the Robert S. Engelmore Memorial Award. In 2012 Etzioni was featured as GeekWire's "Geek of the Week". In 2013 Etzioni was voted "Geek of the Year" through GeekWire. In 2022, Etzioni received the 2012 ACL Test-of-Time Paper Award. In 2022, Etzioni, along with Ana-Maria Popescu and Henry Kautz, received the ACM Intelligent User Interfaces Most Impact Award for their 2003 paper, "Towards a Theory of Natural Language Interfaces to Databases". == Personal life == Etzioni has three children, and has said in interviews that family is his number one priority. He is married to Ivone Etzioni, and was previously married to Dr. Ruth Etzioni, a biostatistician at the Fred Hutchinson Cancer Center. Outside of his professional career, Etzioni has a wide range of personal interests. He has attended the Burning Man festival, which he described as a valuable way to step outside his comfort zone. His first computer was a TRS-80, and he has described his car’s GPS as his favorite gadget, joking that he has “no sense of direction.” == Selected publications == === Scholarly publications === Etzioni, Oren (July 1994). "A Softbot-based Interface to the Internet" (PDF). Communications of the ACM. Retrieved March 29, 2018. Etzioni, Oren (December 2008). "Open Information Extraction from the Web" (PDF). Communications of the ACM. Retrieved March 29, 2018. Zamir, Oren; Etzioni, Oren (1998). "Web document clustering". Proceedings of the 21st annual international ACM SIGIR conference on Research and development in information retrieval. ACM. pp. 46–54. doi:10.1145/290941.290956. ISBN 978-1-58113-015-7. S2CID 244069. Zamir, Oren; Etzioni, Oren (May 1999). "Grouper: a dynamic clustering interface to Web search results". Computer Networks. 31 (11–16): 1361–1374. CiteSeerX 10.1.1.31.8216. doi:10.1016/S1389-1286(99)00054-7. S2CID 206134308. Popescu, Ana-Maria; Etzioni, Oren (2005). "Extracting product features and opinions from reviews". Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing - HLT '05. pp. 339–346. doi:10.3115/1220575.1220618. Etzioni, Oren; Cafarella, Michael; Downey, Doug; Popescu, Ana-Maria; Shaked, Tal; Sonderland, Stephen; Weld, Daniel; Yates, Alexander (June 2005). "Unsupervised named-entity extraction from the Web: An experimental study". Artificial Intelligence. 165 (1): 91–134. doi:10.1016/j.artint.2005.03.001. Downey, Doug; Etzioni, Oren; Sonderland, Stephen (July 2010). "Grouper: Analysis of a probabilistic model of redundancy in unsupervised information extraction". Artificial Intelligence. 174 (11): 726–748. CiteSeerX 10.1.1.174.2441. doi:10.1016/j.artint.2010.04.024. === Popular articles === Etzioni, Oren (August 4, 2011). "Web Search Needs a Shakeup" (PDF). Nature. Retrieved November 21, 2019. Etzioni, Oren (December 9, 2014). "AI Won't Exterminate Us – It Will Empower Us". Backchannel. Retrieved March 29, 2018. Etzioni, Oren (February 4, 2016). "To Keep AI Safe -- Use AI". Vox. Retrieved November 21, 2019. Etzioni, Oren (April 8, 2016). "Quora Session with Oren Etzioni". Quora. Retrieved March 29, 2018. Etzioni, Oren (June 15, 2016). "Deep Learning Isn't a Dangerous Magic Genie. It's Just Math". Wired. Retrieved March 29, 2018. Etzioni, Oren (September 20, 2016). "No, the Experts Don't Think Superintelligent AI is a Threat to Humanity". MIT Technology Review. Retrieved November 21, 2019. Etzioni, Oren (July 6, 2017). "Artificial intelligence: AI Zooms in on highly influential citations". Nature. Retrieved March 29, 2018. Etzioni, Oren (September 1, 2017). "How to Regulate Artificial Intelligence". The New York Times. Retrieved March 29, 2018. Etzioni, Oren (November 2, 2017). "Workers Displaced by Automation Should Try A New Job: Caregiver". Wired. Retrieved March 29, 2018. Etzioni, Oren (March 14, 2018). "A Hippocratic Oath for artificial intelligence practitioners". Tech Crunch. Retrieved March 29, 2018. Etzioni, Oren (March 7, 2018). "A 'Manhattan Project' for science research". The Hill. Retrieved November 21, 2019. Etzioni, Ore