AI Engine Research Articles

AI’s social sciences deficit

To create less harmful technologies and ignite positive social change, AI engineers need to enlist ideas and expertise from a broad range of social science disciplines, including those embracing qualitative methods, say Mona Sloane and Emanuel Moss.

Development and clinical validation of Lantern Pharma’s AI engine: Response algorithm for drug positioning and rescue (RADR).

3114 Background: The Response Algorithm for Drug positioning and Rescue (RADR) technology is Lantern Pharma's proprietary Artificial Intelligence (Al)-based machine learning approach for biomarker identification and patient stratification. RADR is a combination of three automated modules working sequentially to generate drug- and tumor type-specific gene signatures predictive of response. Methods: RADR integrates genomics, drug sensitivity and systems biology inputs with supervised machine learning strategies and generates gene expression-based responder/ non-responder profiles for specific tumor indications with high accuracy, in addition to identification of new correlations of genetic biomarkers with drug activity. Pre-treatment patient gene expression profiles along with corresponding treatment outcomes were used as algorithm inputs. Model training was typically performed using an initial set of genes derived from cancer cell line data when available, and further applied to patient data for model tuning, cross-validation and final gene signature development. Model testing and performance computation were carried out on patient records held out as blinded datasets. Response prediction accuracy and sensitivity were among the model performance metrics calculated. Results: On average, RADR achieved a response prediction accuracy of 80% during clinical validation. We present retrospective analyses performed as part of RADR validation using more than 10 independent datasets of patients from selected cancer types treated with approved drugs including chemotherapy, targeted therapy and immunotherapy agents. For an instance, the application of the RADR program to a Paclitaxel trial in breast cancer patients could have potentially reduced the number of patients in the treatment arm from 92 unselected patients to 24 biomarker-selected patients to produce the same number of responders. Also, we cite published evidence correlating genes from RADR derived biomarkers with increased Paclitaxel sensitivity in breast cancer. Conclusions: The value of RADR platform architecture is derived from its validation through the analysis of about ~17 million oncology-specific clinical data points, and ~1000 patient records. By implementing unique biological, statistical and machine learning workflows, Lantern Pharma's RADR technology is capable of deriving robust biomarker panels for pre-selecting true responders for recruitment into clinical trials which may improve the success rate of oncology drug approvals.

Using single conspiracy number for long term position evaluation

Single Conspiracy Number (SCN) is a variant concept of conspiracy number and proof number which indicates the difficulty of a root node changing its MIN/MAX value to a certain score. It makes up the drawbacks of conspiracy number on computing complexity, and can be easily applied into different sea rch frameworks. This paper explores the potential usage of SCN as a long-term position evaluation to understand in-depth game progress patterns. Chinese Chess is chosen as a testbed for this study, whereas a strong AI engine ‘Xiangqi Wizard’ is used. It is implemented with alpha-beta search and modified to produce SCNs during the search process. Experiments are conducted on different types of positions including tactical positions, drawn positions and opening positions. The experimental results show that SCN is more consistent and accurate for long-term position evaluation than the conventional way using evaluation function values only, and using SCN together with evaluation function values enables us to better understand game progress patterns.

Humanoid Receptionist Connected to IoT Subsystems and Smart Infrastructure is Smarter than Expected

The authors of this paper have the unique opportunity to design the AI engine of a humanoid front desk office (receptionist) employee. The designed AI engine conception is strongly influenced by the Smart Infrastructure approach, making the robot be just an interface to a complex system cooperating with various databases, subsystems and IoT modules and sensors. The WeegreOne robot has been given the access to a wide variety of preferred conversation topics, additional facts, databases and sensors, making it become a valuable interlocutor. The prototype has been installed, and the AI engine have adapted to most of the real-case uses. Surprisingly, human interlocutors neither do not use nor discover the full functionality and versatility of the robot. The research team extended the usability and connectivity of the robot while analyzing the interactions with the employees and visitors of the company. The article presents the concept, design and final thoughts on the results of the experiment - that people seem not to need robotic employees to be really smart or versatile -or - people seem to underestimate the versatility of robotic employees.

AI は AI 技術者を倫理的な設計に巻き込むことができるか

AI は AI 技術者を倫理的な設計に巻き込むことができるか

Advanced Metrology and Intelligent Quality Automation for Industry 4.0-Based Precision Manufacturing Systems

Intelligence is an essential feature of future development and production systems, and intelligent production is a major component of future business. To meet market demands in present and future global industrial world, manufacturing enterprises of any kind and any size must be flexible and agile enough to respond quickly to product demand changes also according technological developments especially in the field of precision engineering at micro/nanoand pico scale production. With support of AI and modern IT it is possible to realise modern cost-effective customer-driven design and manufacturing taking into account the importance and basic role of modern Integrated Management Systems - IMS and intelligent advanced metrology.This new concept can be developed on the basis of intelligent production technologies and integrated systems as well as extensive use of the IT, AI, simulation, quality autonomation, robotics, advanced metrology and advanced engineering data exchange techniques. Moreover by utilizing advanced information analytics, networked intelligent machines and instruments will be able to perform more efficiently, collaboratively and sustainably, that makes possible an agile and optimal industrial production in any kind of industry and especially in up-to-date SMEs towards Industry 4.0.

人工知能エンジン「KIBIT」を用いた自然言語処理と特許調査への応用

人工知能エンジン「KIBIT」を用いた自然言語処理と特許調査への応用

PARADIGM SHIFT: ENGINEERING ARTIFICIAL INTELLIGENCE AND MANAGEMENT STRATEGIES FUSION

The purpose of this paper is to investigate management strategies that use Artificial Intelligence to perceive, capture, and process real-time data to predict and direct the performance of an enterprise. AI systems can account for errors in human judgment through computational processes that supersede the capabilities of human intelligence alone. Qualitative methodology was used to assess components such as fuzzy logic that can produce answers determined by multiple factors that can be integrated into a determinant solution. Quantifying the neurocircuitry of strategic management processes in tacticians will serve as the foundation for AI and management strategies engineering fusion. When AI is programmed to motivate, interact, and make judgements based upon statistical measurements, the fusion of AI and management engineering can increase efficiency and effectiveness in the attainment of organizational goals. Results demonstrated the application of AI planning can range from directing large-scale machinery overhaul procedures, spacecraft mission planning, emergency response, assembling test procedures for rocket launchers, and delivery truck scheduling. A thorough understanding AI for strategic management engineering fusion and its underlying concepts is a prerequisite to competitive advantage in a global market.

Can avatars pass the Turing test? Intelligent agent perception in a 3D virtual environment

The current study involved the first natural language, modified Turing Test in a 3D virtual environment. One hundred participants were given an avatar-guided tour of a virtual clothing store housed in the 3D world of Second Life. In half of the cases, a human research assistant controlled the avatar-guide; in the other half, the avatar-guide was a visually indistinguishable virtual agent or “bot” that employed a chat engine called Discourse, a more robust variant of Artificial Intelligence Markup Language (AIML). Both participants and the human research assistant were blind to variations in the controlling agency of the guide. The results indicated that 78% of participants in the artificial intelligence condition incorrectly judged the bot to be human, significantly exceeding the 50% rate that one would expect by chance alone that is used as the criterion for passage of a modified Turing Test. An analysis of participants׳ decision-making criteria revealed that agency judgments were impacted by both the quality of the AI engine and a number of psychological and contextual factors, including the naivety of participants regarding the possible presence of an intelligent agent, the duration of the trial period, the specificity and structure of the test situation, and the anthropomorphic form and movements of the agent. Thus, passage of the Turing Test is best viewed not as the sole product of advances in artificial intelligence or the operation of psychological and contextual variables, but as a complex process of human–computer interaction.

Reflections on 25+ years of knowledge acquisition

In this paper I give a short reflection on Knowledge Acquisition as a subfield of AI and Knowledge Engineering over the last 25 years or so. My major message is that knowledge modeling is an underrated but still important method to reduce the complexity problems that arise in constructing knowledge-based applications. Scale – as apparent in the Semantic Web – is another important parameter in recent developments in Knowledge Acquisition: it requires other techniques than those of the 1980s. Natural Language Processing is the most promising way forward, but also the most difficult source of the acquisition of formalized knowledge. I will argue that some of the lessons learned in building knowledge-based systems may carry over to reasoning in the Semantic Web and to knowledge acquisition from natural language web sources.

Adaptive AI in a racing simulator: a challenging and fun environment for any user

Racing simulators require Artificial Intelligence in order to create a competitive environment for the player. However these engines always seem to be created in a strict manner, leading to what can be a repetitive, predictable or frustrating system. Difficulty levels are often set in stone and do not suit every player. Development of an adaptive racing AI engine could eradicate the need for difficulty levels and provide a competitive and entertaining racing environment for any user. This paper will discuss the current techniques used in racing simulators, research into adaptive AI, and describe approaches used towards the development of an Adaptive Engine.

A comparative investigation of non-linear activation functions in neural controllers for search-based game AI engineering

The creation of intelligent video game controllers has recently become one of the greatest challenges in game artificial intelligence research, and it is arguably one of the fastest-growing areas in game design and development. The learning process, a very important feature of intelligent methods, is the result of an intelligent game controller to determine and control the game objects behaviors' or actions autonomously. Our approach is to use a more efficient learning model in the form of artificial neural networks for training the controllers. We propose a Hill-Climbing Neural Network (HillClimbNet) that controls the movement of the Ms. Pac-man agent to travel around the maze, gobble all of the pills and escape from the ghosts in the maze. HillClimbNet combines the hill-climbing strategy with a simple, feed-forward artificial neural network architecture. The aim of this study is to analyze the performance of various activation functions for the purpose of generating neural-based controllers to play a video game. Each non-linear activation function is applied identically for all the nodes in the network, namely log-sigmoid, logarithmic, hyperbolic tangent-sigmoid and Gaussian. In general, the results shows an optimum configuration is achieved by using log-sigmoid, while Gaussian is the worst activation function.

AI Bridges and Dreams

This paper is a modified version of my acceptance lecture for the 1986 SPL-Insight Award. It turned into something of a personal credo -describing my view of the nature of AI the potential social benefit of applied AI the importance of basic AI research the role of logic and the methodology of rational construction the interplay of applied and basic AI research, and the importance of funding basic AI. These points are knitted together by an analogy between AI and structural engineering: in particular, between building expert systems and building bridges.

Computational Models of Ethical Reasoning: Challenges, Initial Steps, and Future Directions

How can machines support or, even more significantly, replace humans in performing ethical reasoning? This question greatly interests machine ethics researchers. Imbuing a computer with the ability to reason about ethical problems and dilemmas is as difficult a task as there is for AI scientists and engineers. The author briefly describes a few of the programs and discusses in detail two programs, both of which employ techniques from the area of AI known as case-based reasoning and implement aspects of the ethical approach known as casuistry. One of these programs, Truth-Teller, accepts a pair of ethical dilemmas and describes the salient similarities and differences between them, from both an ethical and a pragmatic perspective. The other program, SIROCCO, accepts a single ethical dilemma and retrieves other cases and ethical principles that might be relevant

Pragmatism, Artificial Intelligence, and Posthuman Bioethics: Shusterman, Rorty, Foucault

Michel Foucault's early works criticize the development of modern democratic institutions as creating a “surveillance society,” which functions to control bodies by making them feel watched and monitored full time. His later works attempt to recover private space by exploring subversive techniques of the body and language. Following Foucault, pragmatists like Richard Shusterman and Richard Rorty have also developed very rich approaches to this project, extending it deeper into the literary and somatic dimensions of self-stylizing. Yet, for a debate centered on the re-creation of the vision of the self, these discussions have yet to fully engage—even while they set the conditions for this engagement with—issues of posthuman technologies, such as AI, robotics, and genetic engineering. These certainly will constitute the primary engine of public institutional surveillance in the years to come. With this surveillance, the two spheres of private self-fashioning and public institutions will continue to evolve in relation to one another. As they do so, democratic society will change considerably. Perhaps Rorty's own normative and linguistic ideal of conversational maintenance may provide something of a democratic limit case on any future posthuman self-fashioning.

Putting AI in entertainment: an AI authoring tool for simulation and games

We have been working on techniques to write better AI behavior for simulations and games. We are focusing on a visual authoring tool that provides a way to quickly synthesize complex behavior, and are building a corresponding AI engine to run with a simulation or game. For game development uses, we see our tool as making the AI understandable to game designers and end users, as well as improving developers' productivity. This work will also be useful for simulation developers and subsequently for analysts, operators, and instructors. We explain some of our tool's features and how the AI engine processes the resulting content.

Artificial Intelligence and Software Engineering: Breaking the Toy Mold

The application of AI techniques to software engineering has suffered, from the perspective of practising software engineers, due to a tradition of testing ideas and theories on small, toy domains. At the IJCAI-95 Workshop on AI and Software Engineering we focused on this issue, and here we discuss some of the results of that workshop, identifying the major weaknesses in AI&SE research, and offer some insight into the future of the field.

A fundamental and theoretical framework for an intelligent CAD system

Currently, there exists a line of research in mechanical CAD that is directed towards using AI and knowledge engineering ideas, but truly unifying approaches in this respect are lacking. The authors hope to fill this gap via a logic-based, theoretical approach. A formulation is given for how a designer's apprentice (or a design workbench) can be established. The relevance of naive physics and commonsense reasoning in machine design are demonstrated. As for the software development methodology several aspects of software engineering are considered. A design base language which is built upon logic programming and object-oriented programming paradigm is proposed.

Programming in schools: 10 years on

Abstract Progress in school computer programming is examined in relation to early claims about the benefits which would accrue from the introduction of programming courses into the school curriculum. The implications of the methodological origins of school programming in both AI and software engineering are discussed. The difficulties of teaching programming and the consequent development of teaching strategies are reviewed. It is concluded that early claims about the development of high‐level thinking skills have not yet been supported. Nor have suggestions that children develop structured programming habits by using an interactive programming environment been borne out by the experiences of the 1980s. However, it is also concluded that to abandon programming in schools on the basis of these experiences would be premature.

Knowledge Acquisition for Expert Systems: A Practical Handbook

Building an expert system involves eliciting, analyzing, and interpreting the that a human expert uses when solving problems. Expe rience has shown that this process of knowledge is both difficult and time consuming and is often a major bottleneck in the production of expert Unfortunately, an adequate theoretical basis for acquisition has not yet been established. This re quires a classification of domains and problem-solving tasks and an improved understanding of the relationship between structures in human and machine. In the meantime, expert system builders need access to information about the techniques currently being employed and their effectiveness in different applications. The aim of this book, therefore, is to draw on the experience of AI scientists, cognitive psychologists, and engineers in discussing particular acquisition techniques and providing practical advice on their application. Each chapter provides a detailed description of a particular technique or methodology applied within a selected task domain. The relative strengths and weaknesses of the tech nique are summarized at the end of each chapter with some suggested guidelines for its use. We hope that this book will not only serve as a practical handbook for expert system builders, but also be of interest to AI and cognitive scientists who are seeking to develop a theory of acquisition for expert systems.