The document discusses artificial intelligence and provides an overview of key topics including:
- A brief history of AI beginning with the 1956 Dartmouth conference where the field was first proposed.
- Types of AI such as artificial weak intelligence, artificial hybrid intelligence, and artificial strong intelligence.
- Applications of AI such as computer vision, machine translation, and robotics.
- Progress in deep learning including speech recognition, computer vision, and machine translation.
- Demos of AI services including a cognitive race between AWS and Azure and using an AWS bot with Lex.
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
Artificial Intelligence (AI) is one of the hottest topics in the tech and startup world at the moment. The field of AI and its associated technologies present a range of opportunities – as well as challenges – for corporates. Learn more about what Artificial Intelligence means for your organization.
Introduction to artifcial intelligence
Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving"
1. Introduction
2. How AI originated
3. Interesting facts about AI
4. Real-life application of AI
5. AI tools
6. Something special
7. Limitations of AI
8. Conclusion
History of AI, Current Trends, Prospective TrajectoriesGiovanni Sileno
Talk given at the 2nd Winter Academy on Artificial Intelligence and International Law of the Asser Institute. The birth of AI: Dartmouth workshop. The biggest AI waves: classic symbolic AI (reasoning, knowledge systems, problem-solving), machine learning (induction). Current problems: explainability, trustworthyness, impact and transformation on society and people, the rise of artificially dumber systems.
Artificial Intelligence (AI) is one of the hottest topics in the tech and startup world at the moment. The field of AI and its associated technologies present a range of opportunities – as well as challenges – for corporates. Learn more about what Artificial Intelligence means for your organization.
Introduction to artifcial intelligence
Artificial intelligence (AI) is intelligence demonstrated by machines, unlike the natural intelligence displayed by humans and animals, which involves consciousness and emotionality. The distinction between the former and the latter categories is often revealed by the acronym chosen. 'Strong' AI is usually labelled as AGI (Artificial General Intelligence) while attempts to emulate 'natural' intelligence have been called ABI (Artificial Biological Intelligence). Leading AI textbooks define the field as the study of "intelligent agents": any device that perceives its environment and takes actions that maximize its chance of successfully achieving its goals.[3] Colloquially, the term "artificial intelligence" is often used to describe machines (or computers) that mimic "cognitive" functions that humans associate with the human mind, such as "learning" and "problem solving"
1. Introduction
2. How AI originated
3. Interesting facts about AI
4. Real-life application of AI
5. AI tools
6. Something special
7. Limitations of AI
8. Conclusion
Branch of computer science that develops machines and software with human-like intelligence
top 5 artificial intelligence stocks
artificial intelligence technology
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artificial intelligence wikipedia
Title: Incredible developments in Artificial intelligence which was the future scenario.
Here I discussed the with the major backbones of AI (Machine learning, Neural networks) types Machine learning and type of Artificial intelligence and with some real-time examples of AI and ML & Benefits and Future of AI with some pros and Cons of Artificial Intelligence.
What really is Artificial Intelligence about? Harmony Kwawu
AI systems are growing. But what is AI, where did the idea behind it come from, what is intelligence, how does expert level intelligence work, and perhaps most importantly, would AI systems eventually make human beings redundant?
Artificial Intelligence - It's meaning, uses, past and future.
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans
Artificial Intelligence (A.I) and Its Application -SeminarBIJAY NAYAK
this presentation includes the the Basics of Artificial Intelligence and its applications in various Field. feel free to ask anything. Editors are always welcome.
Human intelligence is the intellectual powers of humans, Learning
Decision Making
Solve Problems
Feelings(Love,Happy,Angry)
Understand
Apply logic
Experience
making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.
Robots are autonomous or semi-autonomous machines meaning that they can act independently of external commands. Artificial intelligence is software that learns and self-improves.
Why Artificial Intelligence?
• Computers can do computations, by fixed programmed rules
• A.I machines perform tedious tasks efficiently & reliably.
• computers can’t understanding & adapting to new situations.
• A.I aims to improve machine to do such complex tasks.
Advantages of A.I:
Error Reduction
Difficult Exploration(mining & exploration processes)
Daily Application(Siri, Cortana)
Digital Assistants(interact with users)
Medical Applications(Radiosurgery)
Repetitive Jobs(monotonous)
No Breaks
Some disadvantages of A.I:
High Cost
Unemployment
Weaponization
No Replicating Humans
No Original Creativity
No Improvement with Experience
Safety/Privacy Issues
Artificial intelligence will be a Greatest invention Until Machines under the human control. Otherwise The new ERA will be There…..!
AI EXPLAINED Non-Technical Guide for PolicymakersBranka Panic
This guide is meant to help policymakers and citizens understand the basics of Artificial Intelligence (AI) and how it affects our society. It offers explanations and additional resources to help policymakers prepare for the current
and future AI developments.
Branch of computer science that develops machines and software with human-like intelligence
top 5 artificial intelligence stocks
artificial intelligence technology
artificial intelligence articles
artificial intelligence companies
artificial intelligence stocks to buy
artificial intelligence robots
artificial intelligence in medicine
artificial intelligence wikipedia
Title: Incredible developments in Artificial intelligence which was the future scenario.
Here I discussed the with the major backbones of AI (Machine learning, Neural networks) types Machine learning and type of Artificial intelligence and with some real-time examples of AI and ML & Benefits and Future of AI with some pros and Cons of Artificial Intelligence.
What really is Artificial Intelligence about? Harmony Kwawu
AI systems are growing. But what is AI, where did the idea behind it come from, what is intelligence, how does expert level intelligence work, and perhaps most importantly, would AI systems eventually make human beings redundant?
Artificial Intelligence - It's meaning, uses, past and future.
Artificial intelligence is intelligence demonstrated by machines, as opposed to the natural intelligence displayed by animals including humans
Artificial Intelligence (A.I) and Its Application -SeminarBIJAY NAYAK
this presentation includes the the Basics of Artificial Intelligence and its applications in various Field. feel free to ask anything. Editors are always welcome.
Human intelligence is the intellectual powers of humans, Learning
Decision Making
Solve Problems
Feelings(Love,Happy,Angry)
Understand
Apply logic
Experience
making a computer, a computer-controlled robot, or a software think intelligently, in the similar manner the intelligent humans think.
Robots are autonomous or semi-autonomous machines meaning that they can act independently of external commands. Artificial intelligence is software that learns and self-improves.
Why Artificial Intelligence?
• Computers can do computations, by fixed programmed rules
• A.I machines perform tedious tasks efficiently & reliably.
• computers can’t understanding & adapting to new situations.
• A.I aims to improve machine to do such complex tasks.
Advantages of A.I:
Error Reduction
Difficult Exploration(mining & exploration processes)
Daily Application(Siri, Cortana)
Digital Assistants(interact with users)
Medical Applications(Radiosurgery)
Repetitive Jobs(monotonous)
No Breaks
Some disadvantages of A.I:
High Cost
Unemployment
Weaponization
No Replicating Humans
No Original Creativity
No Improvement with Experience
Safety/Privacy Issues
Artificial intelligence will be a Greatest invention Until Machines under the human control. Otherwise The new ERA will be There…..!
AI EXPLAINED Non-Technical Guide for PolicymakersBranka Panic
This guide is meant to help policymakers and citizens understand the basics of Artificial Intelligence (AI) and how it affects our society. It offers explanations and additional resources to help policymakers prepare for the current
and future AI developments.
[Srijan Wednesday Webinars] Artificial Intelligence & the Future of BusinessSrijan Technologies
“AI is the new electricity” – Andrew Ng, former Chief Data Scientist, Baidu
Artificial Intelligence is the new frontier for human evolution. It will upend industries, cause fundamental shifts in processes and jobs, and create unprecedented innovation.The question one wishes to answer is: how and why it impacts industry, and how can it be leveraged by businesses.
This session will introduce AI and machine learning: the process of creating AI, and go on to discuss the key applications of these emerging technologies. We will also dive into a preliminary review of ML algorithms and how they work.
Key Takeaways:
- Define AI and ML, and the philosophy behind these new technologies
- The impact of AI on jobs, communities, business, and industry
- The use cases of AI in different industries like hi-tech, manufacturing, healthcare, publishing and media, education, transportation etc.
-Introduction to machine learning algorithms like classification, regression, neural networks etc.
Check our webinars series and sign up for future webinar notifications at: www.srijan.net/webinar/past-webinars
The training content covers:
- Basics of Artificial Intelligence
- Penetration of AI in our daily lives
- Few examples and Use cases
- A brief on how future with AI looks like
just hvae a look, m sure u whould lyk it...............................................................................................................................................................................its all about artificial machines.....................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................
Infer.NET ist ein in C# geschriebenes open-source Framework, das die Bayes'sche Inferenz mittels probabilistischen Programmierens unter .NET ausführen lässt. Es bietet moderne Algorithmen und Routinen, um intelligente Features in die Apps einzubauen. Infer.NET hat hervorragende Merkmale, die dieses Framework auszeichnen - verschiedene Inferenz-Algorithmen, perfekte Skalierbarkeit, Plattform-Unabhängigkeit und Erweiterbarkeit. Mit modellbasiertem Ansatz lässt sich das Domänenwissen in ein Modell integrieren. Anstatt ein Problem einem bereits vorhandenen Lernalgorithmus zuzuordnen, wird es direkt aus jeweiligem Modell ein maßgeschneiderte ML-Algorithmus erstellt und als C#-Code generiert.
Anwendungsszenarien - Spam- und Daten-Eingabe Prüfung/Hervorsage, Programm-Verifikation, Personalisierung, Empfehlung, Rating, Beurteilung uvm.
Die Teilnehmer werden Infer.NET kennenlernen und dieses hervorragende Werkzeug in Praxis für ein- und ausbauen der intelligenten App-Features einsetzen können.
Maschinelles Lernen (ML / Machine Learning) ist ein essentielles Kernstück der modernen Künstlichen Intelligenz (KI) und hilft unseren Programmen immer besser ("intelligenter") zu werden indem man wiederkehrend Erfahrungen (Daten) sammelt, die durch geschickte ML-Algorithmen viele nützliche Aufgaben wie z.B. Stimmungsanalyse, Objekt-Erkennung und Klassifikation, Preis- bzw. Verkaufsvorhersage uvm. erledigen lassen. Aber was muss man machen, um all das in einem .NET Programm zu ermöglichen - Python lernen, KI-Dienst konsumieren? Nicht unbedingt! Es gibt einen kürzeren Weg - ML.NET als ein open-source und plattformübergreifendes Framework für maschinelles Lernen, das speziell für .NET Entwickler gebaut ist. Damit kann jeder eigene ML-Modelle bauen, ohne vertrautes .NET Eco-System verlassen zu müssen. Teilnehmer dieser Session werden erfahren wie man typische KI-Aufgaben mit ML.NET schnell und einfach löst, spezifische Modelle mit einem brandneuen AutoML-Werkzeug ML.NET Model Builder erstellt und eine Brücke zu populären ML-Frameworks wie z.B. Tensorflow, Infer.NET, ONNX uvm. baut.
Künstliche Intelligenz - Chatbots uns Sprachassistenten. Azure Bot Service.Mykola Dobrochynskyy
Presentation (in German) about Chatbots on Microsoft Azure, Amazon AWS, Google Cloud and IBM Cloud (Bluemix) Platforms and Assistants like Alexa oder Google Asisstant. Azure Bot Service Session.
Presentation (in German) about Chatbots on Microsoft Azure, Amazon AWS, Google Cloud and IBM Cloud (Bluemix) Platforms and Assistants like Alexa oder Google Asisstant. Intro-Session.
Opendatabay - Open Data Marketplace.pptxOpendatabay
Opendatabay.com unlocks the power of data for everyone. Open Data Marketplace fosters a collaborative hub for data enthusiasts to explore, share, and contribute to a vast collection of datasets.
First ever open hub for data enthusiasts to collaborate and innovate. A platform to explore, share, and contribute to a vast collection of datasets. Through robust quality control and innovative technologies like blockchain verification, opendatabay ensures the authenticity and reliability of datasets, empowering users to make data-driven decisions with confidence. Leverage cutting-edge AI technologies to enhance the data exploration, analysis, and discovery experience.
From intelligent search and recommendations to automated data productisation and quotation, Opendatabay AI-driven features streamline the data workflow. Finding the data you need shouldn't be a complex. Opendatabay simplifies the data acquisition process with an intuitive interface and robust search tools. Effortlessly explore, discover, and access the data you need, allowing you to focus on extracting valuable insights. Opendatabay breaks new ground with a dedicated, AI-generated, synthetic datasets.
Leverage these privacy-preserving datasets for training and testing AI models without compromising sensitive information. Opendatabay prioritizes transparency by providing detailed metadata, provenance information, and usage guidelines for each dataset, ensuring users have a comprehensive understanding of the data they're working with. By leveraging a powerful combination of distributed ledger technology and rigorous third-party audits Opendatabay ensures the authenticity and reliability of every dataset. Security is at the core of Opendatabay. Marketplace implements stringent security measures, including encryption, access controls, and regular vulnerability assessments, to safeguard your data and protect your privacy.
Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...John Andrews
SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation"
Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults
Description:
Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project.
Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Explore our comprehensive data analysis project presentation on predicting product ad campaign performance. Learn how data-driven insights can optimize your marketing strategies and enhance campaign effectiveness. Perfect for professionals and students looking to understand the power of data analysis in advertising. for more details visit: https://bostoninstituteofanalytics.org/data-science-and-artificial-intelligence/
3. Agenda
• Motivation
• Overview of the AI and ML
• Cloud and the Intelligent APIs
• Demo 1. Cognitive Race AWS vs. Azure
• Demo 2. AWS Bot with Lex (optional)
• Demo 3. Azure ML Studio
• Demo 4. Alexa Playground
• Mind-Factories Event
• Conclusion
• Q & A
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Artifical Intelligence
4. AI – why we should care?
• According to McKinzey “Automation of knowledge
work” – AI, ML, Natural User Interfaces and BigData
– could have economic impact of $5 - $7 trillion or
110-140 Mio. full-time workers in the next decade.
• According to IDC Big Data will generate about $187
Mio. By 2019 (or +50% vs. 2015). Without ML/AI
most of the Data especially unstructured and short-
living would be lost.
• By 2018 about 50% of developers will embed ML/AI-
Features in their application.
• With democratized Cloud AI-APIs the lean Start-ups
will compete with established companies on the
emerging AI-Markets.
• AI already transforms IT, Communication, Energy,
Financial and Healthcare and soon will transform or
impact almost every industry
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5. AI and 4. Industrial Revolution
Artifical Intelligence is the “electricity”
of the 4. Industrial Revolution
5
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Source: Alan Murray. Fortune.com
6. AI History
6
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On September 2, 1955, the project was formally proposed by McCarthy, Marvin Minsky, Nathaniel
Rochester and Claude Shannon.
“We propose that a 2 month, 10 man study of artificial intelligence be carried out during the summer
of 1956 at Dartmouth College in Hanover, New Hampshire. The study is to proceed on the basis of the
conjecture that every aspect of learning or any other feature of intelligence can in principle be so
precisely described that a machine can be made to simulate it. An attempt will be made to find how to
make machines use language, form abstractions and concepts, solve kinds of problems now reserved
for humans, and improve themselves. We think that a significant advance can be made in one or more
of these problems if a carefully selected group of scientists work on it together for a summer.”
* Timeline-Source: K.E. Park
8. Objective reasons
for the AI-Revolution
• Exponential data growth – the companies
recognized the value of the gathered Big Data
and don’t want to delete or “forget” it (just like
human brain it does).
• Lots of unstructured data – many sensors, IoT
etc. gather tons of unstructured data like audio,
video, environment measurements etc. This
“dark matter” data has to be processed
(visualized) by AI in a meaningful way.
• Lots of short-time living data – i.e. sensor data
used to exchange-prediction of a technical part
becomes useless, when this part is broken.
8
Artifical Intelligence
9. AI “take-off” essential exponents
Besides of profound academic AI theory since mid
50th and objective reasons in field there are 4
essential exponent factors, that make rise of AI
possible:
1. Moor’s Law (CPU / GPU / HPC / Cloud )
2. Big Data (Training-Input & Subject-Goal)
3. Sinking Error-Rate (i.e. IMAGE-Net)
4. AI Investments / Revenues
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10. AI Definition
According to John McCarthy, Artificial Intelligence (AI) is an
information and engineering science dedicated to the
production of "intelligent" machines and especially
"intelligent" computer programs.
The research area wants to use computer intelligence to
understand human intelligence, but does not have to limit
itself to the methods that are observed biologically in
human intelligence. In humans, many animals, and in some
machines, different types and degrees of intelligence occur.
According to McCarthy, the computational part of the
intelligence is the ability to achieve the goals in the world. In
other words, a computer is built and / or programmed
(trained) in such a way that it can independently solve
problems, learn from the mistakes, make decisions, perceive
its surroundings, and communicate with people in a natural
way (for example, linguistically).
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Artifical Intelligence
11. Ontology of the Human Intelligence
11
Artifical Intelligence
Creati-
vity
Facts/Solutions
Predict
Judge
Abstract/Compose
Action
Re-usesolutions
Decide
Experiment
Manipulate
Speak/gesticulate/emotions
Under-
standing
Analyze
Compare/recognize
Search
Translate
Link
Knowledge
Learn
Remember
Discover
Observe
Associate
Sen-
ses
Feel
Hear
See
12. AWI - Artificial weak Intelligence
Artifical weak (or narrow) Intelligence does not solve all, but
only a given narrow range of the human intelligence
ontology. In the case of a narrow AI, the simulation of a
certain range of intelligent behavior with the aid of
mathematics and computer science is concerned.
12
Artifical Intelligence
13. AHI - Artificial hybrid Intelligence
13
Artifical Intelligence
Hybrid artificial intelligence does not solve all but several of
the AI domains in parallel that are crucial for the problem
domain and can be combined with human intelligence and
interaction. This is a combination of several simulations of
intelligent behavior with one another and (in some cases)
with human intelligence.
14. ASI - Artificial strong Intelligence
Artificial strong intelligence aka AI-Singularity has as its goal
to create an artificial intelligence that "mechanizes" human
thinking, consciousness and emotions. Even after decades
of research, the questions of the strong AI are not fully
understood philosophically and the objectives remain
largely visionary.
According to some predictions however AI-Singularity could
be reached in a few decades or even sooner.
As a powerful technology ASI could be very good or very
bad thing for human beings.
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19. Training of the Neural Networks
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Source: https://www.embedded-vision.com
20. Convolutional neural network
(aka CNN)
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Neurons of a
convolutional layer
(blue), connected to their
receptive field (red)
Max pooling with a 2x2
filter and stride = 2
Source: https://en.wikipedia.org/wiki/Convolutional_neural_network
The convolution of f and g is
written f∗g. It is defined as the
integral of the product of the two
functions after one is reversed and
shifted. As such, it is a particular
kind of integral transform
21. Progress in Deep Learning
• Speech recognition
• Computer vision
• Machine translation
• Reasoning, attention and memory
• Reinforcement learning (Games, Go etc.)
• Robotics & control
• Long-term dependencies, very deep nets
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22. ML to AI - Success-Factors
• Lots and lots of data
• Very flexible ML models
• Enough computing power
• Computationally efficient inference
• Powerful predecessors that can beat
dimensionality problem through
compositions (like human abstractions)
• Deep ML Architectures with multiple
levels
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23. From AI to AGI / ASI
• Exponential data growth: big data, weather, science,
entertainment, unstructured and short-living data
• Complexity: climate, energy, resources, economics,
physics etc.
• Solving Al as Artificial General Intelligence (AGI) is
potentially the meta-solution to all these problems
• The goal is to make Al science and/or Al-assisted
science come true
• Artificial Strong Intelligence (ASI) aka AI-Singularity
with human-level and beyond could be a big Meta-
AI-Network of the AI-/AGI-Domains.
• ASI could come faster as we could think! It could be
very powerful and useful (and scary!). So it should be
used ethically and responsibly.
• Philosophical problems of the ASI
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24. AI - products, services and research
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System Provider Type
Microsoft Cognitive Services Microsoft Cloud-Service, AI-API
Google Cloud Machine Learning Plattform Google Cloud-Service, AI-API
Google Assistant Google digital AI-Assistant
Deep Mind DeepMind (Google) AI-Research
Brain Team Google AI-Research
Amazon AI Amazon Cloud-Service, AI-API
Echo / Alexa Amazon digital AI-Assistant
IBM Watson IBM Cloud-Service, AI-API
Facebook AI Research Facebook AI-Research
Open AI Open AI AI-Research (non-profit)
api.ai Google / API AI Cloud-Service, AI-API
25. Few Useful Links
• Session-Materials: https://bizzdozer.com/ai
• Azure Cognitive Services: https://www.microsoft.com/cognitive-services
• Amazon Rekognition: https://console.aws.amazon.com/rekognition
• Deep Learning Online-Book: http://www.deeplearningbook.org
• Deep Mind Home: https://deepmind.com/
• Open-source AI Library: https://www.tensorflow.org
• Software Factories Home: http://www.soft-fact.de
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29. Conclusion
• You need concrete AI-Plan / Strategy (like for Mobile
in the past decade “Mobile first” goes to “AI First”) in
order to keep pace with competitors.
• AI converts Information into Knowledge and
programmers into data scientists.
• AI learns differently as a human – AI with training on
the Big-Data an the human with small chunks of
data, learned experiences and abstractions as well as
from genome derived information.
• Most of the value (by now) is generated by
supervised learning models (i.e. cognitive services)
• AI-Singularity is not expected in the near feature, but
things could change quickly (i.e. winning machine-
algorithm for the Go-game was expected at least in
10-15 years, but the big sensation was happened in
Sep. 2016, as AlphaGo-program won)
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30. Thank you! Questions?
30
Mykola Dobrochynskyy is Managing Director of Software
Factories. His focus and interests are Model-driven Software
Development, Code Generation, Artificial Intelligence (AI) and
Machine Learning, as well as Cloud and Service-oriented
Software Architectures.
Artifical Intelligence