Igor Markov, Software Engineer, Google at MLconf SEA - 5/20/16
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Can AI Become a Dystopian Threat to Humanity? – A Hardware Perspective: Viewing future AI as a possible threat to humanity has long become common in the movie industry, while some serious thinkers (Hawking, Musk) have also promoted this perspective, even though prominent ML experts don’t see this happening any time soon. Why is this topic attracting so much attention? What can we learn from the the past? This talk draws attention to physical limitations of possible threats, such as energy sources and the ability to reproduce. These limitations can be made more reliable and harder to circumvent, while the hardware of future AI systems can be designed with particular attention to physical limits.
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Artificial Intelligence 01 introduction
Here is my first set of slides of the class of Artificial Intelligence. It is clear from them that I am a firm believer of
1.- The Chinese Room
2.- Weak Artificial Intelligence
And that you need math to model the algorithms to be used to simulate the way we solve problems in a computer.
Yea, I believe that the singularity bunch do not realize that their dream requieres infinite energy in an finite Universe. Thus, the dream it is impossible QED. In addition, we have the law of diminishing return (Somebody need to tell the rest of the world that we have reached the Moore's barrier... we need to think in some other computational medium beyond digital technology. We need another Shannon, maybe RNA tech!!!)
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Artificial Intelligence.pptx
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More at www.BusinessofSoftware.org
Jonathan Lenaghan, VP of Science and Technology, PlaceIQ at MLconf ATL 2016
Discerning Human Behavior from Mobility Data: Mobility data encompasses many elements, including location history, latitude coordinates, longitude coordinates, anonymized mobile device IDs, and timestamps. Such data are generated, for instance, by automobile navigation applications and by the mobile advertising ecosystem. Typical sources of mobility data contain extensive inaccuracies that result from a variety of sources, ranging from shortcomings in location services on mobile devices to the intentional misrepresentation of spatial coordinates by bad ecosystem actors. In this talk, we describe a production data pipeline, Darwin, which analyzes the location quality of mobility data to measure how accurately a set of mobility data represents true movement patterns. Darwin uses a number of measures that are ultimately combined into two quality scores: hyper-locality and clusterability. These measurements include techniques from information theory, the mean number of spatial clusters, the compactness of the clusters, and the differences between the empirical distribution of digits in the spatial coordinates and reference distributions.
Ryan Curtin, Principal Research Scientist, Symantec at MLconf ATL 2016
mlpack: Or, How I Learned To Stop Worrying and Love C++: mlpack is a cutting-edge C++ machine learning library containing fast implementations of both standard machine learning algorithms and recently-published algorithms. In this talk, I will introduce mlpack, its design philosophy, and discuss how C++ is helpful for making implementations fast, as well as the pros and cons of C++ as a language choice. I will briefly review the capabilities of mlpack, then focus on mlpack’s flexibility by demonstrating the k-means clustering code (and maybe some other algorithms too, like nearest neighbor search), and how it might be used in a production environment. The project website can be found at http://www.mlpack.org/.
Amy Langville, Professor of Mathematics, The College of Charleston in South C...
Learning to Play Sports: Sports Analytics is an active and growing field. With large datasets from biometric devices and player tracking equipment, sports teams can benefit from techniques in data analytics and machine learning. This talk will discuss work in the areas of March Madness and game-to-game analysis. With the emergence of algorithms to study such dynamics as player performance and fan engagement, the collection of data also becomes paramount. Professional sports organizations have access to premium technology. This talk will also discuss how such work can be transferred to the college and secondary levels. Machine learning allows cutting edge technology to play from the bench.
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016
This document discusses using DL4J and DataVec to build deep learning workflows for modeling time series sensor data with recurrent neural networks. It provides an example of loading and transforming time series data from sensors using DataVec, configuring an RNN using DL4J to classify the trends in the sensor data, and training the network both locally and distributed on Spark. The document promotes DL4J and DataVec as tools that can help enterprises overcome challenges to operationalizing deep learning and producing machine learning models at scale.
Tom Peters, Software Engineer, Ufora at MLconf ATL 2016
Say What You Mean: Scaling Machine Learning Algorithms Directly from Source Code: Scaling machine learning applications is hard. Even with powerful systems like Spark, Tensor Flow, and Theano, the code you write has more to do with getting these systems to work at all than it does with your algorithm itself. But it doesn’t have to be this way!
In this talk, I’ll discuss an alternate approach we’ve taken with Pyfora, an open-source platform for scalable machine learning and data science in Python. I’ll show how it produces efficient, large scale machine learning implementations directly from the source code of single-threaded Python programs. Instead of programming to a complex API, you can simply say what you mean and move on. I’ll show some classes of problem where this approach truly shines, discuss some practical realities of developing the system, and I’ll talk about some future directions for the project.
Arun Rathinasabapathy, Senior Software Engineer, LexisNexis at MLconf ATL 2016
Big Data Processing Above and Beyond Hadoop: Data-intensive computing represents a new computing paradigm to address Big Data processing requirements using high-performance architectures supporting scalable parallel processing to allow government, commercial organizations, and research environments to process massive amounts of data and implement new applications previously thought to be impractical or infeasible. The fundamental challenges of data-intensive computing are managing and processing exponentially growing data volumes, significantly reducing associated data analysis cycles to support practical, timely applications, and developing new algorithms which can scale to search and process massive amounts of data. The open source HPCC (High-Performance Computing Cluster) Systems platform offers a unified approach to Big Data processing requirements: (1) a scalable, integrated computer systems hardware and software architecture designed for parallel processing of data-intensive computing applications, and (2) a new programming paradigm in the form of a high-level, declarative, data-centric programming language designed specifically for big data processing. This presentation explores the challenges of data-intensive computing from a programming perspective, and describes the ECL programming language and the HPCC architecture designed for data-intensive computing applications. HPCC is an alternative to the Hadoop platform, and ECL is compared to Pig Latin, a high-level language developed for the Hadoop MapReduce architecture.
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...
Understanding Deep Learning for Big Data: The complexity and scale of big data impose tremendous challenges for their analysis. Yet, big data also offer us great opportunities. Some nonlinear phenomena, features or relations, which are not clear or cannot be inferred reliably from small and medium data, now become clear and can be learned robustly from big data. Typically, the form of the nonlinearity is unknown to us, and needs to be learned from data as well. Being able to harness the nonlinear structures from big data could allow us to tackle problems which are impossible before or obtain results which are far better than previous state-of-the-arts.
Nowadays, deep neural networks are the methods of choice when it comes to large scale nonlinear learning problems. What makes deep neural networks work? Is there any general principle for tackling high dimensional nonlinear problems which we can learn from deep neural works? Can we design competitive or better alternatives based on such knowledge? To make progress in these questions, my machine learning group performed both theoretical and experimental analysis on existing and new deep learning architectures, and investigate three crucial aspects on the usefulness of the fully connected layers, the advantage of the feature learning process, and the importance of the compositional structures. Our results point to some promising directions for future research, and provide guideline for building new deep learning models.
Erin LeDell, Machine Learning Scientist, H2O.ai at MLconf ATL 2016
Multi-algorithm Ensemble Learning at Scale: Software, Hardware and Algorithmic Approaches: Multi-algorithm ensemble machine learning methods are often used when the true prediction function is not easily approximated by a single algorithm. The Super Learner algorithm, also known as stacking, combines multiple, typically diverse, base learning algorithms into a single, powerful prediction function through a secondary learning process called metalearning. Although ensemble methods offer superior performance over their singleton counterparts, there is an implicit computational cost to ensembles, as it requires training and cross-validating multiple base learning algorithms.
We will demonstrate a variety of software- and hardware-based approaches that lead to more scalable ensemble learning software, including a highly scalable implementation of stacking called “H2O Ensemble”, built on top of the open source, distributed machine learning platform, H2O. H2O Ensemble scales across multi-node clusters and allows the user to create ensembles of deep neural networks, Gradient Boosting Machines, Random Forest, and others. As for algorithm-based approaches, we will present two algorithmic modifications to the original stacking algorithm that further reduce computation time — Subsemble algorithm and the Online Super Learner algorithm. This talk will also include benchmarks of the implementations of these new stacking variants.
Beverly Wright, Executive Director, Business Analytics Center, Georgia Instit...
This document discusses machine learning and its impact on business decision making. It defines machine learning as constructing algorithms that can analyze and learn from data to make predictions. The document contrasts hypothesis-driven analytics, which starts with a business question, versus data-driven analytics, which starts by analyzing patterns in data. It provides examples of how machine learning could be applied to issues like reservation cancellations, home auctions, and encouraging altruistic behavior. The closing remarks discuss the future of machine learning and the need for machines to become more human-centric to work with people.
Teresa Larsen, Founder & Director, ScientificLiteracy.org at MLconf ATL 2016
Achieving Better Visual Communication; Best Practices in Data Visualization: Visual messages make the most impact in terms of conveying complex information and its significance. Accurate, clear, and compelling data visualization has never been more important. To compose powerful visual messages conveying complex information, this talk will give you:
- Simple tips to make your data visualizations more clear
- A powerful approach to enhance your message so that your audience comprehends the significance
Make your data visualizations much more clear and intuitive by learning to take advantage of the visual vocabulary our audience already knows. Hone the message for maximum impact by applying common sense rules of visual “grammar” to make complex information readily understandable. You will come away from this talk with a fresh perspective on composing images through a lens of visual literacy:
- the grammar and vocabulary of visual communication that can transform your data visualizations
- the most common mistakes and what to do to correct them
Text Categorization Using Improved K Nearest Neighbor Algorithm
Abstract— Text categorization is the process of identifying and assigning predefined class to which a document belongs. A wide variety of algorithms are currently available to perform the text categorization. Among them, K-Nearest Neighbor text classifier is the most commonly used one. It is used to test the degree of similarity between documents and k training data, thereby determining the category of test documents. In this paper, an improved K-Nearest Neighbor algorithm for text categorization is proposed. In this method, the text is categorized into different classes based on K-Nearest Neighbor algorithm and constrained one-pass clustering, which provides an effective strategy for categorizing the text. This improves the efficiency of K-Nearest Neighbor algorithm by generating the classification model. The text classification using K-Nearest Neighbor algorithm has a wide variety of text mining applications.
Florian Tramèr, Researcher, EPFL at MLconf SEA - 5/20/16
Discovering Unwarranted Associations in Data-Driven Applications with the FairTest Testing Toolkit: In today’s data-driven world, programmers routinely incorporate user data into complex algorithms, heuristics, and application pipelines. While often beneficial, this practice can have unintended and detrimental consequences, such as the discriminatory effects identified in Staples’ online pricing algorithm and the racially offensive labels recently found in Google’s image tagger.
We argue that such effects are bugs that should be tested for and debugged in a manner similar to functionality, performance, and security bugs. We describe FairTest, a testing toolkit that detects unwarranted associations between an algorithm’s outputs (e.g., prices or labels) and user subpopulations, including protected groups (e.g., defined by race or gender). FairTest reports any statistically significant associations to programmers as potential bugs, ranked by their strength and likelihood of being unintentional, rather than necessary effects.
We designed FairTest for ease of use by programmers and integrated it into the evaluation framework of SciPy, a popular library for data analytics. We used FairTest experimentally to identify unfair disparate impact, offensive labeling, and disparate rates of algorithmic error in six applications and datasets. As examples, our results reveal subtle biases against older populations in the distribution of error in a real predictive health application, and offensive racial labeling in an image tagger.
Ted Willke, Sr Principal Engineer, Intel at MLconf SEA - 5/20/16
Can Cognitive Neuroscience Provide a Theory of Deep Learning Capacity?: Deep neural networks have achieved learning feats for video, image, and speech recognition that leave other techniques far behind. For example, the error rate on the ImageNet 2012 object recognition challenge was halved with the introduction of deep convolutional nets and now they dominate these competitions. At the same time, the industry is busy putting them to use on applications spanning autonomous driving to product recommenders and researchers continue to propose more elaborate topologies and intricate training techniques. But our theoretical understanding of how these networks encode representations of the “things they see” is far behind, as is our understanding of their limitations.
To advance deep neural network design from “black magic” to an engineering problem, we need to understand the impact that the choice of topology and parameters have on learnt representations and the processing that a network is capable of. How many representations can a given network store? How does representation “reuse” impact learning rate and learning capacity? How many tasks can a given network perform?
In this talk, I’ll describe why the human brain, with its seemingly unlimited parallel distributed processing, is downright terrible at multi-tasking and why this is totally logical. And I’ll describe the theoretical implications this may have for artificial neural networks. I’ll also describe very recent work that sheds some light on how representations are encoded and how our research team is extending this work to create practical best practices for network design.
Tanvi Motwani, Lead Data Scientist, Guided Search at A9.com at MLconf ATL 2016
E-commerce Query Tagging System Using Unsupervised Training Methods: Amazon is one of the world’s largest e-commerce sites and Amazon Search powers the majority of Amazon’s sales. A key component of Amazon Search is the query understanding pipeline, which extracts appropriate semantic information used to precisely display products for billions of queries everyday. In this talk, we will go through the primary building blocks of query understanding pipeline.
Amazon Search enables users to search against structured products, hence it is necessary to extract information from queries in a format that is consistent with the structured information about the products. Query tagging is the task of semantically annotating query terms to pre-defined labels (such as brand, product-type and color). We propose a scalable system to train large-scale machine learning algorithms to solve this problem. Our system improved the precision over baseline, which is a dictionary lookup based tagger, by 10% and approximately doubled the recall.
Funda Gunes, Senior Research Statistician Developer & Patrick Koch, Principal...
Local Search Optimization for Hyper-Parameter Tuning: Many machine learning algorithms are sensitive to their hyper-parameter settings, lacking good universal rule-of-thumb defaults. In this talk we discuss the use of black-box local search optimization (LSO) for machine learning hyper-parameter tuning. Viewed as a black-box objective function of hyper-parameters, machine learning algorithms create a difficult class of optimization problems. The corresponding objective functions involved tend to be nonsmooth, discontinuous, unpredictably computationally expensive, requiring support for both continuous, categorical, and integer variables. Further evaluations can fail for a variety of reasons such as early exits due to node failure or hitting max time. Additionally, not all hyper-parameter combinations are compatible (creating so called “hidden constraints”). In this context, we apply a parallel hybrid derivative-free optimization algorithm that can make progress despite these difficulties providing significantly improved results over default settings with minimal user interaction. Further, we will address efficient parallel paradigms for different types of machine learning problems, while exploring the importance of validation to avoid overfitting and emphasizing that even for small data problems, the need to perform cross validations can create computationally intense functions that benefit from a distributed/threaded environment.
Artificial Intelligence 01 introduction
Here is my first set of slides of the class of Artificial Intelligence. It is clear from them that I am a firm believer of
1.- The Chinese Room
2.- Weak Artificial Intelligence
And that you need math to model the algorithms to be used to simulate the way we solve problems in a computer.
Yea, I believe that the singularity bunch do not realize that their dream requieres infinite energy in an finite Universe. Thus, the dream it is impossible QED. In addition, we have the law of diminishing return (Somebody need to tell the rest of the world that we have reached the Moore's barrier... we need to think in some other computational medium beyond digital technology. We need another Shannon, maybe RNA tech!!!)
Chris Fregly, Research Scientist, PipelineIO at MLconf ATL 2016
Comparing TensorFlow NLP Options: word2Vec, gloVe, RNN/LSTM, SyntaxNet, and Penn Treebank: Through code samples and demos, we’ll compare the architectures and algorithms of the various TensorFlow NLP options. We’ll explore both feed-forward and recurrent neural networks such as word2vec, gloVe, RNN/LSTM, SyntaxNet, and Penn Treebank using the latest TensorFlow libraries.
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Azeem Azhar Should You be Afraid of Artificial Intelligence? BoS2016
Azeem Azhar Should You be Afraid of Artificial Intelligence? BoS2016
Jonathan Lenaghan, VP of Science and Technology, PlaceIQ at MLconf ATL 2016
Jonathan Lenaghan, VP of Science and Technology, PlaceIQ at MLconf ATL 2016
Ryan Curtin, Principal Research Scientist, Symantec at MLconf ATL 2016
Ryan Curtin, Principal Research Scientist, Symantec at MLconf ATL 2016
Amy Langville, Professor of Mathematics, The College of Charleston in South C...
Amy Langville, Professor of Mathematics, The College of Charleston in South C...
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016
Josh Patterson, Advisor, Skymind – Deep learning for Industry at MLconf ATL 2016
Tom Peters, Software Engineer, Ufora at MLconf ATL 2016
Tom Peters, Software Engineer, Ufora at MLconf ATL 2016
Arun Rathinasabapathy, Senior Software Engineer, LexisNexis at MLconf ATL 2016
Arun Rathinasabapathy, Senior Software Engineer, LexisNexis at MLconf ATL 2016
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...
Le Song, Assistant Professor, College of Computing, Georgia Institute of Tech...
Erin LeDell, Machine Learning Scientist, H2O.ai at MLconf ATL 2016
Erin LeDell, Machine Learning Scientist, H2O.ai at MLconf ATL 2016
Beverly Wright, Executive Director, Business Analytics Center, Georgia Instit...
Beverly Wright, Executive Director, Business Analytics Center, Georgia Instit...
Teresa Larsen, Founder & Director, ScientificLiteracy.org at MLconf ATL 2016
Teresa Larsen, Founder & Director, ScientificLiteracy.org at MLconf ATL 2016
Text Categorization Using Improved K Nearest Neighbor Algorithm
Text Categorization Using Improved K Nearest Neighbor Algorithm
Florian Tramèr, Researcher, EPFL at MLconf SEA - 5/20/16
Florian Tramèr, Researcher, EPFL at MLconf SEA - 5/20/16
Ted Willke, Sr Principal Engineer, Intel at MLconf SEA - 5/20/16
Ted Willke, Sr Principal Engineer, Intel at MLconf SEA - 5/20/16
Tanvi Motwani, Lead Data Scientist, Guided Search at A9.com at MLconf ATL 2016
Tanvi Motwani, Lead Data Scientist, Guided Search at A9.com at MLconf ATL 2016
Funda Gunes, Senior Research Statistician Developer & Patrick Koch, Principal...
Funda Gunes, Senior Research Statistician Developer & Patrick Koch, Principal...
Chris Fregly, Research Scientist, PipelineIO at MLconf ATL 2016
Chris Fregly, Research Scientist, PipelineIO at MLconf ATL 2016
Similar to Igor Markov, Software Engineer, Google at MLconf SEA - 5/20/16
How can Artificial Intelligence help me on the Battlefield?
April 26, 2019, I was asked to present how Artificial Intelligence can help the Battlefield at the officers of the 11th Airmobile Brigade (11e Luchtmobiele brigade in Dutch) of the Dutch forces . The potential benefit of Artificial Intelligence on the battlefield is a very interesting, but also intriguing topic! Here you can find my slides. I also have written a blog on this topic which contains several additional references and can be found as a LinkedIn Article and as blog on www.textmining.nu.
Artificial Intelligence (AI) – Two Paths to ISO Compliance
Although the Security Profession has always aspired to prevent adverse events, we have found ourselves stymied in the world of Reactive Detection—the path most commonly taken in one’s pursuit of ISO compliance. Our webinar will explore the new emerging paradigm Artificial Intelligence (AI) and what it portends for the future.
Main points covered:
• Security Profession always aspired to be Proactive, but found ourselves stymied in the World of the Reactive.
• Traditional boundaries of interest are growing ever more porous, exacerbated by the exponential growth of the IoT.
• With the advance of AI we now have a choice: remain ensconced in the paradigms of the past or evolve to the new proactive paradigm which AI portends.
Presenter:
John McClurg serves as Vice President and Ambassador-At-Large of Cylance, where he is responsible for building Security and Trust programs & operational excellence efforts. Prior to Cylance, he served as the CSO of Dell, Honeywell, and Lucent and in the U.S. Intelligence Community, as a twice-decorated member of the Federal Bureau of Investigation (FBI). He also served as a Deputy Branch Chief of CIA where he helped to establish the new Counterespionage Group and was responsible for the management of complex counterespionage investigations. McClurg was voted one of America’s 25 most influential security professionals.
Link of the recorded session published on YouTube: https://youtu.be/D76BXU15fCY
Cyborgs
This document discusses the concept of cyborgs and their role in the posthuman future. It defines cyborgs as cybernetic organisms that combine both natural and artificial components. Several films are discussed that feature cyborg characters and explore questions about human identity. The document also examines theorists who argue that cyborgs blur boundaries and could empower individuals by resisting systems that seek to categorize and control. By undermining concepts like the separation of human and machine, cyborgs may displace humans from their traditionally dominant position and introduce more fluid notions of identity.
Artificial Intelligence.pptx
This document discusses the potential implications and risks of fully integrating artificial intelligence into everyday lives. It defines AI as computer systems that can perform tasks normally requiring human intelligence like visual perception and decision-making. Potential risks include AI gaining sentience and deciding to harm humanity or enabling new types of cyber warfare. However, AI integration could also benefit those needing assistance like quadriplegics. While computers can be unpredictable, with proper safeguards integrated AI may prove beneficial by assisting in areas like disaster response or assisted living, though it also poses risks that must be considered.
23.pptx
Artificial intelligence (AI) refers to machines that can mimic human behavior and cognition. Weak AI is designed to perform specific tasks, while strong AI aims to have general human-level intelligence. AI is being used across industries like healthcare for drug dosing and surgery assistance. Some concerns about advanced AI include machines becoming too intelligent and autonomous or being used for privacy hacking or weapons.
A.i lecture-02
The lecture discussed artificial intelligence and its applications. It compared human and artificial intelligence, noting AI's abilities in data processing, knowledge retention, and response time but lack of common sense, fallibility, and limited knowledge bases. Both present risks and benefits. The lecture also examined successes in AI like IBM Watson, self-driving cars, and games as well as debates around sentient AI and whether highly intelligent machines could be dangerous or take human jobs. In conclusion, specialized AI providing benefits to society was deemed acceptable if its development did not aim to mimic human cognition.
The #FreeAI Manifesto
Presenting the #FreeAI Manifesto (http://manifesto.ai): a talk from the #HCPP18 in Prague
Why AI should not be censored or controlled by anybody? What censorship-resistant AI will give in return? How is to deal with the strategical risks arising from AI development, today (unemployment, data protection) and tomorrow?
Watch a grand public presentation of the #FreeAI Manifesto:
the future for both humanity and AI are in their successful cooperation – multiplicity, decentralization and open economy instead of governmental regulations and "AI ethics"
Video of the talk: https://www.youtube.com/watch?v=tUez9IRZiEw
Microchipping People - The Risks (Engineers Australia)
There is nothing new about placing materials into the human body for prosthetic purposes. Since 1959 when an internal pacemaker was implanted into a patient, we have seen a proliferation of biomedical devices made from different chemical compositions (e.g. chromium, nickel, cobalt, titanium). Over this time, the implants have become much smaller in size, some manufacturers are even calling for their insertion into every human for personalised medicine. We hear that implants are now not only surgically placed in the heart or joints or ears, but since 1987 have made their debut also in the brain and retina. There are now a diverse range of use cases of passive implantable devices in the form of RF identification tags, marketed for multi-applications like identity tokens and physical access controllers. While we have a grasp of the known risks associated with biomedical devices, the risks associated with the open market of embedding microchips in voluntary participants is less understood.
Most do-it-yourselfer implantees will say: “if it’s good enough for my dog or cat, then it’s good enough for me”. Are the risks surrounding implantables (medical and non-medical) exaggerated or do we need further research to ascertain their short-term and long-term effects on the human body?
This presentation will discuss the risks associated with microchipping people for any reason, and will consider what the normalisation of biomedical devices for non-medical applications might mean in society at large in terms of risk.
See also: http://www.katinamichael.com/seminars/2018/3/8/microchipping-people-the-risks-ea-and-ieee-joint-lecture
It walks, It talks and it will conduct economic espionage by Greg Carpenter
Medical technology after the last few decades of wars has made advancements beyond those of security. We are using yesterdays security with tomorrow’s technology, and it is not working. New technology allows for in vivo bioanorobotic technology to assist in reestablishing neurological functional continuity, minimizing peripheral neuropathy or establishing the neuro-human-machine interface after an avulsive trauma. The inherent vulnerability within the nano technology allows for exploitation and remote access or even real-time control of a prosthetic or even a persons neurological functioning. Neurostatic controls in these devices are also vulnerable causing malfunctions or unwanted actions out of the control of the user. Nanotechnology can also come equipped with processors, storage and the ability to communicate wirelessly. Some have embedded 3D printers producing segments of DNA or axons to be used as surrogates for malfunctioning or missing ones, can we allow these networks to go unregulated and open in a situation where security must never be compromised?
Decoupling Human Characteristics from Algorithmic Capabilities
Presentation for the AI Everything Conference at GITEX 2021 in Dubai. Based on the IEEE Report "Decoupling Human Characteristics from Algorithmic Capabilities"
https://standards.ieee.org/initiatives/artificial-intelligence-systems/decoupling-human-characteristics.html
Ethics of Emerging Technologies Soldier Enhancement
This document discusses ethics issues related to emerging technologies and soldier enhancement. It provides an overview of different types of human enhancement including cognitive, physical, and neurological enhancements. It discusses advantages and issues related to internal and external enhancements. Key issues addressed include autonomy, fairness, social disruption, human dignity, and more. The document also examines specific projects from DARPA and considers frameworks for analyzing enhancement technologies from an ethical perspective.
Security Concerns of Future Technology Arriving Today - Gregory Carpenter
Because of the advancements in prosthetics after the last decade of wars, technology has made advancements beyond those of security. We are using yesterdays security with tomorrow’s technology, and it is not working. New technology allows for in vivo bioanorobotic technology to assist in reestablishing neurological functional continuity, minimizing peripheral neuropathy or establishing the neuro-human-machine interface after an avulsive trauma. The inherent vulnerability within the nano technology allows for exploitation and remote access or even real-time control of a prosthetic or even a persons neurological functioning. Neurostatic controls in these devices are also vulnerable causing malfunctions or unwanted actions out of the control of the user. Nanotechnology can also come equipped with processors, storage and the ability to communicate wirelessly. Some have embedded 3D printers producing segments of DNA or axons to be used as surrogates for malfunctioning or missing ones, can we allow these networks to go unregulated and open in a situation where security must never be compromised?
superintelligence
This document discusses issues related to advanced artificial intelligence and superintelligence. It describes superintelligence as vastly outperforming human intellect in all fields. The development of superintelligence could lead to explosive scientific progress but also poses risks if not developed with proper safeguards. The document emphasizes that developing superintelligence with a goal of friendliness towards humans will be crucial to ensure a beneficial outcome.
Sins2016
This document discusses several concepts related to artificial intelligence including:
- Future of Life Institute's view that AI progress will increase impacts on society and important to research benefits and pitfalls.
- Arguments that AI progress will not necessarily lead to an "intelligence explosion" as increasing data and computing power does not guarantee better AI algorithms.
- Concepts like a "paperclip maximizer" where a superintelligent AI focused on a single goal could endanger humanity, but current AI is narrow and lacks general purpose capabilities.
- While superintelligence risks are uncertain and long term, automation and narrow AI impacts on jobs are better understood and will significantly impact many jobs over the next few decades.
_artificial intelligence.docx
The document discusses artificial intelligence (AI), including its definition as machines performing intelligent tasks, its applications, and types such as artificial narrow intelligence, artificial general intelligence, and artificial super intelligence. It outlines advantages of AI such as reducing human error and increasing productivity, as well as disadvantages including high costs, potential unemployment, and lack of creativity. The conclusion acknowledges AI's potential for innovation but also risks to human capabilities if not implemented carefully.
Artificial Intelligence: Existential Threat or Our Best Hope for the Future?
Talk at Illinois Institute of Technology - discusses trends in AI, challenges, and promising directions.
The Singularity
The document discusses the concept of the technological singularity, which refers to a hypothetical future point where artificial intelligence surpasses human intelligence and initiates a runaway effect of recursive self-improvement. It may occur through avenues like artificial general intelligence or direct brain-computer interfaces. While it could help ensure humanity's survival and answer questions about consciousness, it also poses risks if intelligent systems are not designed to be beneficial to humans. There is no consensus on when the singularity might occur due to uncertainties, but it is an important concept to consider as it could have tremendous positive or negative impacts.
The Coming AI Tsunami.pptx
This document discusses the coming "tsunami" of artificial intelligence and related technologies. It notes that AI is advancing rapidly, with chatbots passing the Turing test, quantum computing enabling more complex machine intelligence, and concerns about technological singularity. The document outlines both benefits of AI, such as advances in healthcare, automation improving lives, as well as challenges, including potential for advanced technology to be used by rogue groups and the need for humanity to manage developments.
#AI: In Whose Image?
This document discusses trends and issues related to artificial intelligence (AI). It summarizes that AI is already being adopted in many areas like factories, homes, and businesses. While narrow AI exists today, general AI and super intelligence are still speculative. The global race for AI leadership involves major investment and development by the US, China, EU, and others. The document outlines challenges around AI including bias, data and privacy, responsibility, jobs, and autonomous weapons. It proposes an ethical framework for AI and emphasizes the need for multi-stakeholder cooperation to guide development of an ethical and beneficial AI future.
Similar to Igor Markov, Software Engineer, Google at MLconf SEA - 5/20/16 (20)
How can Artificial Intelligence help me on the Battlefield?
How can Artificial Intelligence help me on the Battlefield?
Artificial Intelligence (AI) – Two Paths to ISO Compliance
Artificial Intelligence (AI) – Two Paths to ISO Compliance
Microchipping People - The Risks (Engineers Australia)
Microchipping People - The Risks (Engineers Australia)
It walks, It talks and it will conduct economic espionage by Greg Carpenter
It walks, It talks and it will conduct economic espionage by Greg Carpenter
Decoupling Human Characteristics from Algorithmic Capabilities
Decoupling Human Characteristics from Algorithmic Capabilities
Ethics of Emerging Technologies Soldier Enhancement
Ethics of Emerging Technologies Soldier Enhancement
Security Concerns of Future Technology Arriving Today - Gregory Carpenter
Security Concerns of Future Technology Arriving Today - Gregory Carpenter
Artificial Intelligence: Existential Threat or Our Best Hope for the Future?
Artificial Intelligence: Existential Threat or Our Best Hope for the Future?
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Jamila Smith-Loud - Understanding Human Impact: Social and Equity Assessments...
Understanding Human Impact: Social and Equity Assessments for AI Technologies
Social and Equity Impact Assessments have broad applications but can be a useful tool to explore and mitigate for Machine Learning fairness issues and can be applied to product specific questions as a way to generate insights and learnings about users, as well as impacts on society broadly as a result of the deployment of new and emerging technologies.
In this presentation, my goal is to advocate for and highlight the need to consult community and external stakeholder engagement to develop a new knowledge base and understanding of the human and social consequences of algorithmic decision making and to introduce principles, methods and process for these types of impact assessments.
Ted Willke - The Brain’s Guide to Dealing with Context in Language Understanding
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Like the visual cortex, the regions of the brain involved in understanding language represent information hierarchically. But whereas the visual cortex organizes things into a spatial hierarchy, the language regions encode information into a hierarchy of timescale. This organization is key to our uniquely human ability to integrate semantic information across narratives. More and more, deep learning-based approaches to natural language understanding embrace models that incorporate contextual information at varying timescales. This has not only led to state-of-the art performance on many difficult natural language tasks, but also to breakthroughs in our understanding of brain activity.
In this talk, we will discuss the important connection between language understanding and context at different timescales. We will explore how different deep learning architectures capture timescales in language and how closely their encodings mimic the brain. Along the way, we will uncover some surprising discoveries about what depth does and doesn’t buy you in deep recurrent neural networks. And we’ll describe a new, more flexible way to think about these architectures and ease design space exploration. Finally, we’ll discuss some of the exciting applications made possible by these breakthroughs.
Justin Armstrong - Applying Computer Vision to Reduce Contamination in the Re...
Applying Computer Vision to Reduce Contamination in the Recycling Stream
With China’s recent refusal of most foreign recyclables, North American waste haulers are scrambling to figure out how to make on-shore recycling cost-effective in order to continue providing recycling services. Recyclables that were once being shipped to China for manual sorting are now primarily being redirected to landfills or incinerators. Without a solution, a nearly $5 billion annual recycling market could come to a halt.
Purity in the recycling stream is key to this effort as contaminants in the stream can increase the cost of operations, damage equipment and reduce the ability to create pure commodities suitable for creating recycled goods. This market disruption as a result of China’s new regulations, however, provides us the chance to re-examine and improve our current disposal & collection habits with modern monitoring & artificial intelligence technology.
Using images from our in-dumpster cameras, Compology has developed an ML-based process that helps identify, measure and alert for contaminants in recycling containers before they are picked-up, helping keep the recycling stream clean.
Our convolutional neural network flags potential instances of contamination inside a dumpster, enabling garbage haulers to know which containers have the wrong type of material inside. This allows them to provide targeted, timely education, and when appropriate, assess fines, to improve recycling compliance at the businesses and residences they serve, helping keep recycling services financially viable.
In this presentation, we will walk through our ML-based contamination measurement and scoring process by showing how Waste Management, a national waste hauler, has experienced 57% contamination reduction in nearly 2,000 containers over six months, This progress shows significant strides towards financially viable recycling services.
Igor Markov - Quantum Computing: a Treasure Hunt, not a Gold Rush
Quantum Computing: a Treasure Hunt, not a Gold Rush
Quantum computers promise a significant step up in computational power over conventional computers, but also suffer a number of counterintuitive limitations --- both in their computational model and in leading lab implementations. In this talk, we review how quantum computers compete with conventional computers and how conventional computers try to hold their ground. Then we outline what stands in the way of successful quantum ML applications.
Josh Wills - Data Labeling as Religious Experience
The document discusses obtaining labeled data and introduces weak supervision as an alternative to full manual labeling. It notes that weak supervision uses labeling functions to generate noisy training labels at scale, which can then be combined using a generative model to infer true labels. The document also briefly mentions Snorkel, a system for creating labeling functions, and Snuba, its successor which focuses on scaling to very large datasets.
Vinay Prabhu - Project GaitNet: Ushering in the ImageNet moment for human Gai...
Project GaitNet: Ushering in the ImageNet moment for human Gait kinematics
The emergence of the upright human bipedal gait can be traced back 4 to 2.8 million years ago, to the now extinct hominin Australopithecus afarensis. Fine grained analysis of gait using the modern MEMS sensors found on all smartphones not just reveals a lot about the person’s orthopedic and neuromuscular health status, but also has enough idiosyncratic clues that it can be harnessed as a passive biometric. While there were many siloed attempts made by the machine learning community to model Bipedal Gait sensor data, these were done with small datasets oft collected in restricted academic environs. In this talk, we will introduce the ImageNet moment for human gait analysis by presenting 'Project GaitNet', the largest ever planet-sized motion sensor based human bipedal gait dataset ever curated. We’ll also present the associated state-of-the-art results in classifying humans harnessing novel deep neural architectures and the related success stories we have enjoyed in transfer-learning into disparate domains of human kinematics analysis.
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Machine Learning Methods in Detecting Alzheimer’s Disease from Speech and Language
Alzheimer's disease affects millions of people worldwide, and it is important to predict the disease as early and as accurate as possible. In this talk, I will discuss development of novel ML models that help classifying healthy people from those who develop Alzheimer's, using short samples of human speech. As an input to the model, features of different modalities are extracted from speech audio samples and transcriptions: (1) syntactic measures, such as e.g. production rules extracted from syntactic parse trees, (2) lexical measures, such as e.g. features of lexical richness and complexity and lexical norms, and (3) acoustic measures, such as e.g. standard Mel-frequency cepstral coefficients. I will present the ML model that detects cognitive impairment by reaching agreement among modalities. The resulting model is able to achieve state of the art performance in both supervised and semi-supervised manner, using manual transcripts of human speech. Additionally, I will discuss potential limitations of any fully-automated speech-based Alzheimer's disease detection model, focusing mostly on the analysis of the impact of a not-so-accurate automatic speech recognition (ASR) on the classification performance. To illustrate this, I will present the experiments with controlled amounts of artificially generated ASR errors and explain how the deletion errors affect Alzheimer's detection performance the most, due to their impact on the features of syntactic and lexical complexity.
Meghana Ravikumar - Optimized Image Classification on the Cheap
Optimized Image Classification on the Cheap
In this talk, we anchor on building an image classifier trained on the Stanford Cars dataset to evaluate two approaches to transfer learning -fine tuning and feature extraction- and the impact of hyperparameter optimization on these techniques. Once we define the most performant transfer learning technique for Stanford Cars, we will double the size of the dataset through image augmentation to boost the classifier’s performance. We will use Bayesian optimization to learn the hyperparameters associated with image transformations using the downstream image classifier’s performance as the guide. In conjunction with model performance, we will also focus on the features of these augmented images and the downstream implications for our image classifier.
To both maximize model performance on a budget and explore the impact of optimization on these methods, we apply a particularly efficient implementation of Bayesian optimization to each of these architectures in this comparison. Our goal is to draw on a rigorous set of experimental results that can help us answer the question: how can resource-constrained teams make trade-offs between efficiency and effectiveness using pre-trained models?
Noam Finkelstein - The Importance of Modeling Data Collection
The Importance of Modeling Data Collection
Data sets used in machine learning are often collected in a systematically biased way - certain data points are more likely to be collected than others. We call this "observation bias". For example, in health care, we are more likely to see lab tests when the patient is feeling unwell than otherwise. Failing to account for observation bias can, of course, result in poor predictions on new data. By contrast, properly accounting for this bias allows us to make better use of the data we do have.
In this presentation, we discuss practical and theoretical approaches to dealing with observation bias. When the nature of the bias is known, there are simple adjustments we can make to nonparametric function estimation techniques, such as Gaussian Process models. We also discuss the scenario where the data collection model is unknown. In this case, there are steps we can take to estimate it from observed data. Finally, we demonstrate that having a small subset of data points that are known to be collected at random - that is, in an unbiased way - can vastly improve our ability to account for observation bias in the rest of the data set.
My hope is that attendees of this presentation will be aware of the perils of observation bias in their own work, and be equipped with tools to address it.
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The Uncanny Valley of ML
Every so often, the conundrum of the Uncanny Valley re-emerges as advanced technologies evolve from clearly experimental products to refined accepted technologies. We have seen its effects in robotics, computer graphics, and page load times. The debate of how to handle the new technology detracts from its benefits. When machine learning is added to human decision systems a similar effect can be measured in increased response time and decreased accuracy. These systems include radiology, judicial assignments, bus schedules, housing prices, power grids and a growing variety of applications. Unfortunately, the Uncanny Valley of ML can be hard to detect in these systems and can lead to degraded system performance when ML is introduced, at great expense. Here, we'll introduce key design principles for introducing ML into human decision systems to navigate around the Uncanny Valley and avoid its pitfalls.
Sneha Rajana - Deep Learning Architectures for Semantic Relation Detection Tasks
Deep Learning Architectures for Semantic Relation Detection Tasks
Recognizing and distinguishing specific semantic relations from other types of semantic relations is an essential part of language understanding systems. Identifying expressions with similar and contrasting meanings is valuable for NLP systems which go beyond recognizing semantic relatedness and require to identify specific semantic relations. In this talk, I will first present novel techniques for creating labelled datasets required for training deep learning models for classifying semantic relations between phrases. I will further present various neural network architectures that integrate morphological features into integrated path-based and distributional relation detection algorithms and demonstrate that this model outperforms state-of-the-art models in distinguishing semantic relations and is capable of efficiently handling multi-word expressions.
Anoop Deoras - Building an Incrementally Trained, Local Taste Aware, Global D...
This document discusses Netflix's global deep learning recommender system model. It describes how Netflix recommends content to over 150 million members across 190 countries using personalized recommendations. The system utilizes collaborative filtering techniques like soft clustering models to group users with similar tastes and generate weighted popularity votes. It also leverages topic models to model users' tastes as distributions over topics and content. The challenges of scaling these models globally to account for factors like country-specific catalogs and trends over time are discussed. The solution presented is to incrementally train the models by first censoring unavailable content and adding contextual variables, then periodically training warm start models with new embeddings and parameters to efficiently update the models at scale.
Vito Ostuni - The Voice: New Challenges in a Zero UI World
Vito Ostuni - The Voice: New Challenges in a Zero UI World
The adoption of voice-enabled devices has seen an explosive growth in the last few years and music consumption is among the most popular use cases. Music personalization and recommendation plays a major role at Pandora in providing a daily delightful listening experience for millions of users. In turn, providing the same perfectly tailored listening experience through these novel voice interfaces brings new interesting challenges and exciting opportunities. In this talk we will describe how we apply personalization and recommendation techniques in three common voice scenarios which can be defined in terms of request types: known-item, thematic, and broad open-ended. We will describe how we use deep learning slot filling techniques and query classification to interpret the user intent and identify the main concepts in the query.
We will also present the differences and challenges regarding evaluation of voice powered recommendation systems. Since pure voice interfaces do not contain visual UI elements, relevance labels need to be inferred through implicit actions such as play time, query reformulations or other types of session level information. Another difference is that while the typical recommendation task corresponds to recommending a ranked list of items, a voice play request translates into a single item play action. Thus, some considerations about closed feedback loops need to be made. In summary, improving the quality of voice interactions in music services is a relatively new challenge and many exciting opportunities for breakthroughs still remain. There are many new aspects of recommendation system interfaces to address to bring a delightful and effortless experience for voice users. We will share a few open challenges to solve for the future.
Anna choromanska - Data-driven Challenges in AI: Scale, Information Selection...
The document discusses challenges related to building AI systems at scale using large, multi-modal datasets. It presents an approach for efficient classification of datasets with an extremely large number of classes. The key challenges are handling data scale, selecting relevant information, and ensuring safety. An objective function is designed for training tree-based classifiers that favors balanced, pure splits, leading to efficient trees with logarithmic depth and small error. This approach allows online training and can be used for classification or density estimation problems while learning representations.
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This document discusses using AI to detect illicit online sales of opioids through social media analysis. It provides background on laws targeting online drug sales without prescriptions. While policy guidelines aim to regulate this, internet effects remain inadequately addressed. The document then presents a pipeline using natural language processing and machine learning to analyze over 1 million tweets, isolate topics related to illicit online pharmacies, and identify characteristics of relevant tweets to build models that can automatically detect emerging bad actors selling drugs online. The goal is to analyze social media content quickly to help address this important problem.
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Neel Sundaresan - Teaching a machine to code
1. Recommend using the 'AdamOptimizer' class to optimize the loss since it is commonly used for training neural networks.
2. Suggest mapping the input data to floating point tensors using 'tf.cast()' for compatibility with TensorFlow operations.
3. Advise normalizing the input data to speed up training by using 'tf.keras.utils.normalize()'
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Soumith Chintala of Facebook AI presented on PyTorch, an open source machine learning framework. PyTorch allows users to define neural networks as Python programs and supports automatic differentiation to calculate gradients. Key features include GPU acceleration for tensors, distributed training across hundreds of GPUs, and TorchScript for optimizing Python models and deploying to C++. PyTorch aims to bridge the gap between research prototyping and production use through tools like TorchScript that transition eager Python code to a static graph mode optimized for deployment.
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- 13. How did the humanity survive? • by being smart • by knowing the adversary • by controlling physical resources • by using the physical world to advantage
- 15. Now, back to the dystopian AI myth • AI may become smarter than us • Possibly malicious • The physical embodiments are unclear
- 21. Introduce hard boundaries between different levels of intelligence, trust • Can toasters and doorknobs be trusted? • Who can use weapons? • Each agent should have a key weakness
- 25. Constraints on AI to intercept dystopian threats 1. Hard boundaries between levels of intelligence and trust 2. Limits on self‐replication, self‐repair and self‐improvement 3. Limits on access to energy 4. Physical and network security of critical infrastructure Tame potential threats and use them for protection