Category Archives: Artificial Intelligence

Which is Which?

It all started out as science fiction: machines that can talk, machines that can think, machines that can feel. Although that last bit may be impossible without sparking an entire world of debate regarding the existence of consciousness, scientists have certainly been making strides with the first two.

Over the years, we have been hearing a lot about artificial intelligence, machine learning, and deep learning. But how do we differentiate between these three rather abstruse terms, and how are they related to one another?

Artificial intelligence (AI) is the general field that covers everything that has anything to do with imbuing machines with intelligence, with the goal of emulatinga human beings unique reasoning faculties. Machine learning is a category within the larger field of artificial intelligence that is concerned with conferring uponmachines the ability to learn. This is achieved by using algorithms that discoverpatterns and generate insights from the data they are exposed to, for application to future decision-making and predictions, a process that sidesteps theneed to be programmed specifically for every single possible action.

Deep learning, on the other hand, is a subset of machine learning: its the most advanced AI field, one that brings AI the closest to thegoal of enabling machines to learn and think as much like humans as possible.

In short, deep learning is a subset of machine learning, and machine learning falls within artificial intelligence. The followingimage perfectly encapsulatesthe interrelationship of the three.

Heres a little bit of historical background to better illustrate the differences between the three, and how each discovery and advance has paved the way for the next:

Philosophers attempted to make sense of human thinking in the context of a system, and this idea resulted in the coinage ofthe term artificial intelligence in 1956. And its stillbelieved that philosophy has an important role to play in the advancement of artificial intelligence to this day. Oxford University physicist David Deutsch wrote in an article how he believes that philosophy still holds the key to achieving artificial general intelligence (AGI), the level of machine intelligence comparable to that of the human brain, despite the fact that no brain on Earth is yet close to knowing what brains do in order to achieve any of that functionality.

Advancements in AI have given rise to debates specifically about them being a threat to humanity, whether physically or economically (for which universal basic income is also proposed, and is currently being tested in certain countries).

Machine learning is just one approach to reifyingartificial intelligence, and ultimately eliminates (or greatly reduces) the need to hand-code the software with a list of possibilities, and how the machine intelligence ought toreact to each of them. Throughout 1949 until the late 1960s, American electric engineer Arthur Samuel worked hard onevolving artificial intelligence from merely recognizing patterns to learning from the experience, making him the pioneer of the field. He used a game of checkers for his research while working with IBM, and this subsequently influenced the programming of early IBM computers.

Current applications are becoming more and more sophisticated, making their way into complex medical applications.

Examples include analyzing large genome sets in an effort to prevent diseases, diagnosing depression based on speech patterns, and identifying people with suicidal tendencies.

As we delve into higher and evenmore sophisticated levels of machine learning, deep learning comes into play. Deep learning requires a complex architecture that mimics a human brains neural networks in order to make sense of patterns, even with noise, missing details, and other sources of confusion. While the possibilities of deep learning are vast, so are its requirements: you need big data, and tremendous computing power.

It means not having to laboriously program a prospective AI with that elusive quality of intelligencehowever defined. Instead, all the potential for future intelligence and reasoning powers are latent in the program itself, much like an infants inchoate but infinitely flexible mind.

Watch this video for a basic explanation of how it all works:

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Artificial Intelligence: What It Is and How It Really Works

Theres more to it than meets the eye with Sensor-Track. Our scientists developed the track using a base material of PVC, which was selected for its high tensile strength, flexibility and chemical resistance. This base material was then further re-enforced using an encapsulated micro-weave fabric, to maximise tear resistance while maintaining the desired balance between weight, thickness and strength. We then experimented with custom texture finishes, to determine the optimum amount of friction to allow the cars tyres to grip the track, but allow players to drift, or oversteer with opposite lock, through corners. The brief was to replicate as closely as possible the feel and interaction of a real car on a real race track.

The result is a track system that no matter how big you wish to go, is still portable enough to carry to your friends or the park, in a low weight and compact storage box. No lumpy bits of plastic or metal ensure Sensor-Track can also be truly flat packed for the most efficient stowage. You wont fill a whole wardrobe no matter how large your collection of track pieces becomes and your track and cars are 100% compatible with everyone elses, so you can go large. Very, very large.

Throughout the development we worked closely with model racing enthusiasts to build a modular system of track parts that delivers the maximum flexibility to design and build different race circuits. You can now take Le Mans to your mates, with extra Sensor-Track pieces such as short & sharp (R1) bends for tight circuits and wide sweeping (R2) bends, bottlenecks, and crossovers. Sensor Track allows you to build realistic representations of famous race tracks like Silverstone & Nurburgring! See the Track Builder where building tracks of the world are a reality.

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Real FX - Slotless Racing with Artificial Intelligence

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OpenAI has been backed by tech luminaries to the tune of $1bn.

Microsoft and Elon Musks artificial intelligence research group have signed a partnership.

Terms of the partnership will see OpenAI use Microsofts cloud, Azure, for its large-scale experiments.

The non-profit AI research organisation that is backed by Elon Musk, Peter Thiel, and the likes of Amazon Web Services and Infosys to the tune of $1bn, has the goal of advancing digital intelligence in a way that, is most likely to benefit humanity as a whole, unconstrained by a need to generate financial return.

The partnership comes about partly due to Microsofts work on its Cognitive Toolkit, a

system for deep learning that is used to speed advances in areas such as speech and

image recognition, and because Azure already supports AI workloads with tools such as Azure Batch and Azure Machine Learning.

OpenAI has been an early adopter of Microsofts still in beta N-Series machines, a cloud computing service that is powered by Nvidia and now the two will partner on finding ways to advance AI research.

OpenAI said in a blog post: In the coming months we will use thousands to tens of thousands of these machines to increase both the number of experiments we run and the size of the models we train.

Microsoft has been pushing ahead of the cloud market when it comes to investing in artificial intelligence and introducing it into products. Cortana is probably the most well known of its AI offerings but it has also been applying the technology to medicine and bots, which it has begun rolling out to online help services.

Microsoft also today launched its Azure Bot Service. The service is designed to help developers to cost effectively host their bots on Azure.

OpenAI was created in December 2015 with a group of founding members that includes the like of Elon Musk. Ilya Sutskever is the research director and Greg Brockman is the CTO.

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Elon Musk's artificial intelligence group signs Microsoft ...

Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent. The ability to create intelligent machines has intrigued humans since ancient times and today with the advent of the computer and 50 years of research into AI programming techniques, the dream of smart machines is becoming a reality. Researchers are creating systems which can mimic human thought, understand speech, beat the best human chess player, and countless other feats never before possible. I focused on this area for my capstone because I thought it would be an original idea and also would be interesting to investigate and determine if artificial intelligence is a good concept or a bad to the human life. I wish to accomplish how it came about, the reasoning behind artificial intelligence, and where I think it will go in the future based on my research on this topic. . The Story Behind it All. Artificial intelligence has been around for longer then most people think. We all think that artificial intelligence has been in research for about 20 years or so. In all actuality after thousands of years of fantasy, the appearance of the digital computer, with its native, human-like ability to process symbols, made it seem that the myth of the man-made intelligence would finally become reality. The history of artificial intelligence all started in the 3rd century BC. Chinese engineer Mo Ti created mechanical birds, dragons, and warriors. Technology was being used to transform myth into reality.1. Much later, mechanical ducks and humanoid figures, crafted by clockmakers, endlessly amused the Royal courts of the Enlightenment-age Europe. It has long been possible to make machines that looked and moved in human-like ways.3 Machines that could spook and awe the audience - but creating a model of the mind, in that day in time were off limits.

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FREE Artificial Intelligence Essay - Example Essays

Spring-2005 Classes: TR 5:15 pm - 6:30 pm, RVAC 107 Instructor: Dr. Zdravko Markov, MS 203, (860)-832-2711, http://www.cs.ccsu.edu/~markov/, e-mail: markovz@ccsu.edu Office hours: MW: 6:45 pm - 7:45 pm, TR: 10:00 am - 12:00 pm, or by appointment Catalog Description Artificial Intelligence ~ Spring. ~ [c] ~ Prereq.: CS 253 or (for graduates) CS 501. ~ Presentation of artificial intelligence as a coherent body of ideas and methods to acquaint the student with the classic programs in the field and their underlying theory. Students will explore this through problem-solving paradigms, logic and theorem proving, language and image understanding, search and control methods, and learning. Course Goals

The letter grades will be calculated according to the following table:

Late assignments will be marked one letter grade down for each two classes they are late. It is expected that all students will conduct themselves in an honest manner and NEVER claim work which is not their own. Violating this policy will result in a substantial grade penalty or a final grade of F.

To do the semester projects students have to form teams of 3 people (2-people teams should consult the instructor first). Each team chooses one project to work on. The projects to choose from are the following:

To complete the project students are required to:

Documentation and submission: Write a report describing the solutions to all problems and answers to all questions and mail it as an attachment to my instructors account for the WebCT (available through Campus Pipeline/My Courses/Artificial Intelligence).

Documentation and submission: Write a report describing the solutions to all problems and answers to all questions and mail it as an attachment to my instructors account for the WebCT (available through Campus Pipeline/My Courses/Artificial Intelligence).

Use the weather (tennis) data in tennis.pland do the following:

Documentation and submission: Write a report describing the solutions to all problems and mail it as an attachment to my instructors account for the WebCT (available through Campus Pipeline/My Courses/Artificial Intelligence).

The test includes the following topics:

The test includes the following topics:

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Artificial Intelligence Course - Computer Science at CCSU

Artificial intelligence

Artificial intelligence is technology and a branch of computer science that studies and develops intelligent machines and software. Major AI researchers and textbooks define the field as "the study and design of intelligent agents", where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy, who coined the term in 1955, defines it as "the science and engineering of making intelligent machines". AI research is highly technical and specialised, deeply divided into subfields that often fail to communicate with each other. Some of the division is due to social and cultural factors: subfields have grown up around particular institutions and the work of individual researchers. AI research is also divided by several technical issues. There are subfields which are focused on the solution of specific problems, on one of several possible approaches, on the use of widely differing tools and towards the accomplishment of particular applications. The central problems of AI research include reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. General intelligence is still among the field's long term goals. Currently popular approaches include statistical methods, computational intelligence and traditional symbolic AI. There are an enormous number of tools used in AI, including versions of search and mathematical optimization, logic, methods based on probability and economics, and many others.

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What does artificial intelligence mean? - Definitions.net

For the commander facing an unconventional adversary; for the intelligence analyst trying to find the needle in the data haystack; or for the operator trying to maintain complex systems under degraded conditions or attack, todays warfighter faces problems of scale, complexity pace and resilience that outpace unaided human decision making. Artificial Intelligence (AI) provides the technology to augment human analysis and decision makers by capturing knowledge in computers in forms that can be re-applied in critical situations. This gives users the ability to react to problems that require analysis of massive data; demand fast-paced analysis and decision making, and that demand resilience in uncertain and changing conditions. AI offers the technology to change the human role from in-the-loop controller to on-the-loop thinker who can focus on a more reflective assessment of problems and strategies, guiding rather than being buried in execution detail. By creating technology that allows captured knowledge to continually evolve to incorporate new experience or changing user's needs, AI-based analysis and decision support tools can continue to assist the user long after its original knowledge becomes obsolete.

Key Technologies Artificial Intelligence is focused on the research, development, and transition of technologies that enable dynamic and real-time changes to knowledge bases that allow for informed, agile, and coordinated Command and Control decisions

The Artificial Intelligence group has an emphasis in four key thrust areas:

Artificial Intelligence is one of several Research Areas for the Informatics Laboratory.

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Artificial Intelligence Lockheed Martin

Acknowledgement The material on this page is taken from Chapter 19 of Guide to Medical Informatics, the Internet and Telemedicine (First Edition) by Enrico Coiera (reproduced here with the permission of the author). Introduction

From the very earliest moments in the modern history of the computer, scientists have dreamed of creating an 'electronic brain'. Of all the modern technological quests, this search to create artificially intelligent (AI) computer systems has been one of the most ambitious and, not surprisingly, controversial.

It also seems that very early on, scientists and doctors alike were captivated by the potential such a technology might have in medicine (e.g. Ledley and Lusted, 1959). With intelligent computers able to store and process vast stores of knowledge, the hope was that they would become perfect 'doctors in a box', assisting or surpassing clinicians with tasks like diagnosis.

With such motivations, a small but talented community of computer scientists and healthcare professionals set about shaping a research program for a new discipline called Artificial Intelligence in Medicine (AIM). These researchers had a bold vision of the way AIM would revolutionise medicine, and push forward the frontiers of technology.

AI in medicine at that time was a largely US-based research community. Work originated out of a number of campuses, including MIT-Tufts, Pittsburgh, Stanford and Rutgers (e.g. Szolovits, 1982; Clancey and Shortliffe, 1984; Miller, 1988). The field attracted many of the best computer scientists and, by any measure, their output in the first decade of the field remains a remarkable achievement.

In reviewing this new field in 1984, Clancey and Shortliffe provided the following definition:

Much has changed since then, and today this definition would be considered narrow in scope and vision. Today, the importance of diagnosis as a task requiring computer support in routine clinical situations receives much less emphasis (J. Durinck, E. Coiera, R. Baud, et al., "The Role of Knowledge Based Systems in Clinical Practice," in: eds Barahona and Christenen, Knowledge and Decisions in Health Telematics - The Next Decade, IOS Press, Amsterdam, pp. 199- 203, 1994), So, despite the focus of much early research on understanding and supporting the clinical encounter, expert systems today are more likely to be found used in clinical laboratories and educational settings, for clinical surveillance, or in data-rich areas like the intensive care setting. For its day, however, the vision captured in this definition of AIM was revolutionary.

After the first euphoria surrounding the promise of artificially intelligent diagnostic programmes, the last decade has seen increasing disillusion amongst many with the potential for such systems. Yet, while there certainly have been ongoing challenges in developing such systems, they actually have proven their reliability and accuracy on repeated occasions (Shortliffe, 1987).

Much of the difficulty has been the poor way in which they have fitted into clinical practice, either solving problems that were not perceived to be an issue, or imposing changes in the way clinicians worked. What is now being realised is that when they fill an appropriately role, intelligent programmes do indeed offer significant benefits. One of the most important tasks now facing developers of AI-based systems is to characterise accurately those aspects of medical practice that are best suited to the introduction of artificial intelligence systems.

In the remainder of this chapter, the initial focus will thus remain on the different roles AIM systems can play in clinical practice, looking particularly to see where clear successes can be identified, as well as looking to the future. The next chapter will take a more technological focus, and look at the way AIM systems are built. A variety of technologies including expert systems and neural networks will be discussed. The final chapter in this section on intelligent decision support will look at the way AIM can support the interpretation of patient signals that come off clinical monitoring devices.

In his opinion, there were no ultimately useful measures of intelligence. It was sufficient that an objective observer could not tell the difference in conversation between a human and a computer for us to conclude that the computer was intelligent. To cancel out any potential observer biases, Turing's test put the observer in a room, equipped with a computer keyboard and screen, and made the observer talk to the test subjects only using these. The observer would engage in a discussion with the test subjects using the printed word, much as one would today by exchanging e-mail with a remote colleague. If a set of observers could not distinguish the computer from another human in over 50% of cases, then Turing felt that one had to accept that the computer was intelligent.

Another consequence of the Turing test is that it says nothing about how one builds an intelligent artefact, thus neatly avoiding discussions about whether the artefact needed to in anyway mimic the structure of the human brain or our cognitive processes. It really didn't matter how the system was built in Turing's mind. Its intelligence should only to be assessed based upon its overt behaviour.

There have been attempts to build systems that can pass Turing's test in recent years. Some have managed to convince at least some humans in a panel of judges that they too are human, but none have yet passed the mark set by Turing.

An alternative approach to strong AI is to look at human cognition and decide how it can be supported in complex or difficult situations. For example, a fighter pilot may need the help of intelligent systems to assist in flying an aircraft that is too complex for a human to operate on their own. These 'weak' AI systems are not intended to have an independent existence, but are a form of 'cognitive prosthesis' that supports a human in a variety of tasks.

AIM systems are by and large intended to support healthcare workers in the normal course of their duties, assisting with tasks that rely on the manipulation of data and knowledge. An AI system could be running within an electronic medical record system, for example, and alert a clinician when it detects a contraindication to a planned treatment. It could also alert the clinician when it detected patterns in clinical data that suggested significant changes in a patient's condition.

Along with tasks that require reasoning with medical knowledge, AI systems also have a very different role to play in the process of scientific research. In particular, AI systems have the capacity to learn, leading to the discovery of new phenomena and the creation of medical knowledge. For example, a computer system can be used to analyse large amounts of data, looking for complex patterns within it that suggest previously unexpected associations. Equally, with enough of a model of existing medical knowledge, an AI system can be used to show how a new set of experimental observations conflict with the existing theories. We shall now examine such capabilities in more detail.

Expert or knowledge-based systems are the commonest type of AIM system in routine clinical use. They contain medical knowledge, usually about a very specifically defined task, and are able to reason with data from individual patients to come up with reasoned conclusions. Although there are many variations, the knowledge within an expert system is typically represented in the form of a set of rules.

There are many different types of clinical task to which expert systems can be applied.

Generating alerts and reminders. In so-called real-time situations, an expert system attached to a monitor can warn of changes in a patient's condition. In less acute circumstances, it might scan laboratory test results or drug orders and send reminders or warnings through an e-mail system.

Diagnostic assistance. When a patient's case is complex, rare or the person making the diagnosis is simply inexperienced, an expert system can help come up with likely diagnoses based on patient data.

Therapy critiquing and planning. Systems can either look for inconsistencies, errors and omissions in an existing treatment plan, or can be used to formulate a treatment based upon a patient's specific condition and accepted treatment guidelines.

Agents for information retrieval. Software 'agents' can be sent to search for and retrieve information, for example on the Internet, that is considered relevant to a particular problem. The agent contains knowledge about its user's preferences and needs, and may also need to have medical knowledge to be able to assess the importance and utility of what it finds.

Image recognition and interpretation. Many medical images can now be automatically interpreted, from plane X-rays through to more complex images like angiograms, CT and MRI scans. This is of value in mass-screenings, for example, when the system can flag potentially abnormal images for detailed human attention.

There are numerous reasons why more expert systems are not in routine use (Coiera, 1994). Some require the existence of an electronic medical record system to supply their data, and most institutions and practices do not yet have all their working data available electronically. Others suffer from poor human interface design and so do not get used even if they are of benefit.

Much of the reluctance to use systems simply arose because expert systems did not fit naturally into the process of care, and as a result using them required additional effort from already busy individuals. It is also true, but perhaps dangerous, to ascribe some of the reluctance to use early systems upon the technophobia or computer illiteracy of healthcare workers. If a system is perceived by those using it to be beneficial, then it will be used. If not, independent of its true value, it will probably be rejected.

Happily, there are today very many systems that have made it into clinical use. Many of these are small, but nevertheless make positive contributions to care. In the next two sections, we will examine some of the more successful examples of knowledge-based clinical systems, in an effort to understand the reasons behind their success, and the role they can play.

In the first decade of AIM, most research systems were developed to assist clinicians in the process of diagnosis, typically with the intention that it would be used during a clinical encounter with a patient. Most of these early systems did not develop further than the research laboratory, partly because they did not gain sufficient support from clinicians to permit their routine introduction.

It is clear that some of the psychological basis for developing this type of support is now considered less compelling, given that situation assessment seems to be a bigger issue than diagnostic formulation. Some of these systems have continued to develop, however, and have transformed in part into educational systems.

DXplain is an example of one of these clinical decision support systems, developed at the Massachusetts General Hospital (Barnett et al., 1987). It is used to assist in the process of diagnosis, taking a set of clinical findings including signs, symptoms, laboratory data and then produces a ranked list of diagnoses. It provides justification for each of differential diagnosis, and suggests further investigations. The system contains a data base of crude probabilities for over 4,500 clinical manifestations that are associated with over 2,000 different diseases.

DXplain is in routine use at a number of hospitals and medical schools, mostly for clinical education purposes, but is also available for clinical consultation. It also has a role as an electronic medical textbook. It is able to provide a description of over 2,000 different diseases, emphasising the signs and symptoms that occur in each disease and provides recent references appropriate for each specific disease.

Decision support systems need not be 'stand alone' but can be deeply integrated into an electronic medical record system. Indeed, such integration reduces the barriers to using such a system, by crafting them more closely into clinical working processes, rather than expecting workers to create new processes to use them.

The HELP system is an example of this type of knowledge-based hospital information system, which began operation in 1980 (Kuperman et al., 1990; Kuperman et al., 1991). It not only supports the routine applications of a hospital information system (HIS) including management of admissions and discharges and order entry, but also provides a decision support function. The decision support system has been actively incorporated into the functions of the routine HIS applications. Decision support provide clinicians with alerts and reminders, data interpretation and patient diagnosis facilities, patient management suggestions and clinical protocols. Activation of the decision support is provided within the applications but can also be triggered automatically as clinical data is entered into the patient's computerised medical record.

One of the most successful areas in which expert systems are applied is in the clinical laboratory. Practitioners may be unaware that while the printed report they receive from a laboratory was checked by a pathologist, the whole report may now have been generated by a computer system that has automatically interpreted the test results. Examples of such systems include the following.

Laboratory expert systems usually do not intrude into clinical practice. Rather, they are embedded within the process of care, and with the exception of laboratory staff, clinicians working with patients do not need to interact with them. For the ordering clinician, the system prints a report with a diagnostic hypothesis for consideration, but does not remove responsibility for information gathering, examination, assessment and treatment. For the pathologist, the system cuts down the workload of generating reports, without removing the need to check and correct reports.

All scientists are familiar with the statistical approach to data analysis. Given a particular hypothesis, statistical tests are applied to data to see if any relationships can be found between different parameters. Machine learning systems can go much further. They look at raw data and then attempt to hypothesise relationships within the data, and newer learning systems are able to produce quite complex characterisations of those relationships. In other words they attempt to discover humanly understandable concepts.

Learning techniques include neural networks, but encompass a large variety of other methods as well, each with their own particular characteristic benefits and difficulties. For example, some systems are able to learn decision trees from examples taken from data (Quinlan, 1986). These trees look much like the classification hierarchies discussed in Chapter 10, and can be used to help in diagnosis.

Medicine has formed a rich test-bed for machine learning experiments in the past, allowing scientists to develop complex and powerful learning systems. While there has been much practical use of expert systems in routine clinical settings, at present machine learning systems still seem to be used in a more experimental way. There are, however, many situations in which they can make a significant contribution.

Shortliffe EH. The adolescence of AI in medicine: will the field come of age in the '90s? Artif Intell Med. 1993 Apr;5(2):93-106. Review.

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Artificial Intelligence in Medicine: An Introduction

Shivon Zilis and James Cham, who invest in machine learning-related companies for Bloomberg Beta, recently created a machine intelligence market landscape.

Below, you can find links to the 317+ companies in the landscape (and a few more), and play around with some apps that are applying machine learning in interesting ways.

Algocian Captricity Clarifai Cortica Deepomatic DeepVision Netra Orbital Insight Planet Spaceknow

Capio Clover Intelligence Gridspace MindMeld Mobvoi Nexidia Pop Up Archive Quirious.ai TalkIQ Twilio

Alluvium C3 IoT Planet OS Maana KONUX Imubit GE Predix ThingWorx Uptake Sentenai Preferred Networks

Alation Arimo Cycorp

Deckard.ai Digital Reasoning IBM Watson Kyndi Databricks Sapho

Bottlenose CB Insights DataFox Enigma

Intelligent Layer Mattermark Predata Premise Quid Tracxn

ActionIQ Clarabridge Eloquent Labs Kasisto Preact Wise.io Zendesk

6sense AppZen Aviso Clari Collective[i] Fusemachines InsideSales Salesforce Einstein Zensight

AirPR BrightFunnel CogniCor Lattice LiftIgniter Mintigo msg.ai Persado Radius Retention Science

Cylance Darktrace Deep Instinct Demisto Drawbridge Networks Graphistry LeapYear SentinelOne SignalSense Zimperium

Entelo Algorithmia HiQ HireVue SpringRole Textio Unitive Wade & Wendy

AdasWorks Auro Robotics Drive.ai Google Mobileye nuTonomy Tesla Uber Zoox

Airware DJI DroneDeploy Lily Pilot AI Labs Shield AI Skycatch Skydio

Clearpath Robotics Fetch Robotics Harvest Automation JaybridgeRobotics Kindred AI Osaro Rethink Robotics

Amazon Alexa Apple Siri Facebook M Google Now/Allo Microsoft Cortana Replika

Alien Labs Butter.ai Clara Labs

Deckard.ai SkipFlag Slack Sudo Talla x.ai Zoom.ai

Abundant Robotics AgriData Blue River Technology Descartes Labs Mavrx Pivot Bio TerrAvion Trace Genomics Tule UDIO

AltSchool Content Technologies (CTI) Coursera Gradescope Knewton Volley

AlphaSense Bloomberg Cerebellum Capital Dataminr iSentium Kensho Quandl Sentient

Beagle Blue J Legal Legal Robot Ravel Law ROSS Intelligence Seal

Acerta ClearMetal Marble NAUTO PitStop Preteckt Routific

Calculario Citrine Eigen Innovations Ginkgo Bioworks Nanotronics Sight Machine Zymergen

Affirm Betterment Earnest Lendo Mirador Tala (a InVenture) Wealthfront ZestFinance

Atomwise CareSkore Deep6 Analytics IBM Watson Health Numerate Medical Oncora pulseData Sentrian Zephyr Health

DreamUp Vision

3Scan Arterys Bay Labs Butterfly Network Enlitic Google DeepMind Imagia

Atomwise Color Genomics Deep Genomics Grail iCarbonX Luminist Numerate Recursion Pharmaceuticals Verily Whole Biome

Automat Howdy Kasisto KITT.AI Maluuba Octane AI OpenAI Gym Semantic Machines

Ayasdi BigML Dataiku DataRobot Domino Data Lab Kaggle RapidMiner Seldon

Spark Beyond Yhat Yseop

Bonsai ScaleContext Relevant Cycorp Datacratic deepsense.io Geometric Intelligence H2O.ai HyperScience Loop AI Labs minds.ai Nara LogicsReactive Scaled Inference Skymind SparkCognition

Agolo AYLIEN Cortical.io Lexalytics Loop AI Labs Luminoso MonkeyLearn Narrative Science spaCy

AnOdot Bonsai

Deckard.ai Fuzzy.ai Hyperopt Kite Layer 6 AI Lobe.ai RainforestQA SignifAI SigOpt

Amazon Mechanical Turk CrowdAI CrowdFlower Datalogue DataSift diffbot Enigma Import.io Paxata Trifacta WorkFusion

Amazon DSSTNE Apache Spark Azure ML Baidu Caffe Chainer DeepLearning4j H2O.ai Keras Microsoft CNTK Microsoft DMTK MLlib MXNet Nervana Neon PaddlePaddle scikit-learn TensorFlow Theano Torch7 Weka

1026 Labs Cadence Cirrascale Google TPU Intel (Nervana) Isocline KNUPATH NVIDIA DGX-1/Titan X Qualcomm Tenstorrent Tensilica

Cogitai Kimera Knoggin NNAISENSE Numenta OpenAI Vicarious

Andrew Ng Chief Scientist of Baidu; Chairman and Co-Founder of Coursera; Stanford CS faculty.

Sam Altman President, YC Group, OpenAI co-chairman.

Harry Shum EVP, Microsoft AI and Research.

Geoffrey Hinton The godfather of deep learning.

Samiur Rahman CEO of Canopy. Former Data Engineering Lead at Mattermark.

Jeff Dean Google Senior Fellow at Google, Inc. Co-founder and leader of Googles deep learning research and engineering team.

Eric Horvitz Technical Fellow at Microsoft Research

Denny Britz Deep Learning at Google Brain.

Tom Mitchell Computer scientist and E. Fredkin University Professor at the Carnegie Mellon University.

Chris Dixon General Partner at Andreessen Horowitz.

Hilary Mason Founder at FastForwardLabs. Data Scientist in Residence at Accel.

Elon Musk Tesla Motors, SpaceX, SolarCity, PayPal & OpenAI.

Kirk Borne The Principal Data Scientist at Booz Allen, PhD Astrophysicist.

Peter Skomoroch Co-Founder & CEO SkipFlag. Previously Principal Data Scientist at LinkedIn, Engineer at AOL.

Paul Barba Chief Scientist at Lexalytics.

Andrej Karpathy Research scientist at OpenAI. Previously CS PhD student at Stanford.

Monica Rogati Former VP of Data Jawbone & LinkedIn data scientist.

Xavier Amatriain Leading Engineering at Quora. Netflix alumni.

Mike Gualtieri Forrester VP & Principal Analyst.

Fei-Fei Li Professor of Computer Science, Stanford University, Director of Stanford AI Lab.

David Silver Royal Society University Research Fellow.

Nando de Freitas Professor of Computer ScienceFellow, Linacre College.

Roberto Cipolla Department of Engineering, University of Cambridge.

Gabe Brostow Associate Professor in Computer Science at Londons Global University.

Arthur Gretton Associate Professor with the Gatsby Computational Neuroscience Unit.

Ingmar Posner University Lecturer in Engineering Science at the University of Oxford.

Pieter Abbeel Associate Professor, UC Berkeley, EECS. Berkeley Artificial Intelligence Research (BAIR) laboratory. UC Berkeley Center for Human Compatible AI. Co-Founder Gradescope.

Josh Wills Slack Data Engineering and Apache Crunch committer.

Noah Weiss Head of Search, Learning, & Intelligence at Slack in NYC. Former SVP of Product at foursquare + Google PM on structured search.

Michael E. Driscoll Founder, CEO Metamarkets. Investor at Data Collective

Drew Conway Founder and CEO of Alluvium.

Sean Taylor Facebook Data Science Team

Demis Hassabis Co-Founder & CEO, DeepMind.

Randy Olson Senior Data Scientist at Penn Institute for Biomedical Informatics.

Shivon Zilis Partner at Bloomberg Beta where she focuses on machine intelligence companies.

Adam Gibson Founder of Skymind.

Alexandra Suich Technology reporter for The Economist.

Anthony Goldblum Co-founder and CEO of Kaggle.

Avi Goldfarb Professor at Rotman, University of Toronto and the Chief Data Scientist at Creative Destruction Lab.

Ben Lorica Chief Data Scientist of O'Reilly Media, and Program Director of OReilly Strata & OReillyAI conferences. Ben hosts the OReilly Data Show Podcast too.

Chris Nicholson Co-founder Deeplearning4j & Skymind. Previous to that, Chris worked at The New York Times.

Doug Fulop Product manager at Kindred.ai.

Dror Berman Founder, Innovation Endeavors.

Dylan Tweney Founder of @TweneyMedia, former EIC @venturebeat, ex-@WIRED, publisher of @tinywords.

Gary Kazantsev R&D Machine Learning at Bloomberg LP.

Gideon Mann Head of Data Science / CTO Office at Bloomberg LP.

Gordon Ritter Cloud investor at Emergence Capital, cloud entrepreneur.

Jack Clark Strategy and Communications Director OpenAI. Past: @business Worlds Only Neural Net Reporter. @theregister Distributed Systems Reporter.

Federico Pascual COO & Co-Founder, MonkeyLearn.

Matt Turck VC at FirstMark Capital and the organizer of Data Driven NYC and Hardwired NYC.

Nick Adams Data Scientist, Berkeley Institute for Data Science.

Roger Magoulas Research Director, OReilly Media.

Sean Gourley Former CEO, Quid.

Shruti Gandhi Array.VC, previously at True & Samsung Ventures.

Steve Jurvetson Partner at Draper Fisher Jurvetson.

Vijay Sundaram Venture Capitalist Innovation Endeavors, Tinkerer Polkadot Labs.

Zavain Dar VC Lux Capital, Lecturer Stanford University, Moneyball Philadelphia 76ers.

Yann Lecun Director of AI Research, Facebook. Founding Director of the NYU Center for Data Science

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The Non-Technical Guide to Machine Learning & Artificial ...

Artificial Intelligence (AI) is a field that develops intelligent algorithms and machines. Examples include: self-driving cars, smart cameras, surveillance systems, robotic manufacturing, machine translations, internet searches, and product recommendations. Modern AI often involves self-learning systems that are trained on massive amounts of data ("Big Data"), and/or interacting intelligent agents that perform distributed reasoning and computation. AI connects sensors with algorithms and human-computer interfaces, and extends itself into large networks of devices. AI has found numerous applications in industry, government and society, and is one of the driving forces of today's economy.

The Master's programme in Amsterdam has a technical approach towards AI research. It is a joint programme of the University of Amsterdam and VrijeUniversiteit Amsterdam. This collaboration guarantees a wide range of topics, all taught by world renownedresearchers who are experts in their field.

In this Master's programme we offer a comprehensive collection of courses. It includes:

Next to the general AI programme we offer specialisations in:

Published by GSI

Excerpt from:

Artificial Intelligence - Graduate Schools of Science ...