Artificial intelligence (AI) is an area of computer science that emphasizes the creation of intelligent machines that work and react like humans. Some of the activities computers with artificial intelligence are designed for include: Speech recognition, Learning, Planning and Problem solving - [Source: https://www.techopedia.com/definition/190/artificial-intelligence-ai]
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
Introduction Artificial Intelligence a modern approach by Russel and Norvig 1Garry D. Lasaga
In computer science, artificial intelligence, sometimes called machine intelligence, is intelligence demonstrated by machines, in contrast to the natural intelligence displayed by humans and animals. - Wikipedia
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
AILABS - Lecture Series - Is AI the New Electricity? - Advances In Machine Le...AILABS Academy
Prof. Garain discusses in brief on the backgrounds of learning algorithms & major breakthroughs that have been made in the field of machine perception in the last 50 yrs. He also discusses the role of statistical algorithms like artificial neural network, support vector machines, and other concepts related to Deep Learning algorithms.
Along with the above, Prof. Garain touched upon the basics of CNN & RNN, Long Short-Term Memory Networks (LSTM) & attention network & illustrate all of these using real-life problems. Several state-of-the-art problems like image captioning, visual question answering, medical image analysis etc. were discussed to make the potential of deep learning algorithms understandable.
Prof. Utpal Garain is one of the leading minds in Kolkata in the field of Neural Networks & Artificial Intelligence. His research interest is now focused on AI research, especially exploring deep learning methods for language, image and video analysis including NLP tools, OCRs, handwriting analysis, computational forensics and the like.
The Foundations of Artificial Intelligence, The History of
Artificial Intelligence, and the State of the Art. Intelligent Agents: Introduction, How Agents
should Act, Structure of Intelligent Agents, Environments. Solving Problems by Searching:
problem-solving Agents, Formulating problems, Example problems, and searching for Solutions,
Search Strategies, Avoiding Repeated States, and Constraint Satisfaction Search. Informed
Search Methods: Best-First Search, Heuristic Functions, Memory Bounded Search, and Iterative
Improvement Algorithms.
AILABS - Lecture Series - Is AI the New Electricity? - Advances In Machine Le...AILABS Academy
Prof. Garain discusses in brief on the backgrounds of learning algorithms & major breakthroughs that have been made in the field of machine perception in the last 50 yrs. He also discusses the role of statistical algorithms like artificial neural network, support vector machines, and other concepts related to Deep Learning algorithms.
Along with the above, Prof. Garain touched upon the basics of CNN & RNN, Long Short-Term Memory Networks (LSTM) & attention network & illustrate all of these using real-life problems. Several state-of-the-art problems like image captioning, visual question answering, medical image analysis etc. were discussed to make the potential of deep learning algorithms understandable.
Prof. Utpal Garain is one of the leading minds in Kolkata in the field of Neural Networks & Artificial Intelligence. His research interest is now focused on AI research, especially exploring deep learning methods for language, image and video analysis including NLP tools, OCRs, handwriting analysis, computational forensics and the like.
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.
Overview of epigenetics and its role in diseaseGarry D. Lasaga
Epigenetics is the study of heritable changes in gene expression (active versus inactive genes) that do not involve changes to the underlying DNA sequence — a change in phenotype without a change in genotype — which in turn affects how cells read the genes.
Epigenetics is the study of heritable changes in gene expression (active versus inactive genes) that do not involve changes to the underlying DNA sequence — a change in phenotype without a change in genotype — which in turn affects how cells read the genes. - [https://www.whatisepigenetics.com/fundamentals/]
Author of this presentation: The University of Western Australia
The epithelium lining the respiratory tract from the nasal fossa through the bronchi is called the respiratory mucosa and is characterized by a pseudostratified ciliated epithelium with abundant non-ciliated cells known as goblet cells. - [Source: medcell.med.yale.edu/histology/respiratory_system_lab.php]
Structurally, the skin consists of two layers which differ in function, histological appearance and their embryological origin. The outer layer or epidermis is formed by an epithelium and is of ectodermal origin. ... The skin and its appendages together are called the integumentary system. - [Source: Blue Histology - Integumentary System]
The lymphatic system consists of organs, ducts, and nodes. It transports a watery clear fluid called LYMPH distributes immune cells and other factors throughout the body.
Gene regulation is how a cell controls which genes, out of the many genes in its genome, are "turned on" (expressed). Thanks to gene regulation, each cell type in your body has a different set of active genes – despite the fact that almost all the cells of your body contain the exact same DNA. These different patterns of gene expression cause your various cell types to have different sets of proteins, making each cell type uniquely specialized to do its job. [Source: https://www.khanacademy.org/science/biology/gene-regulation/gene-regulation-in-eukaryotes/a/overview-of-eukaryotic-gene-regulation]
The mitochondrial DNA (mtDNA) is a small circular molecule that codes for some proteins in the respiratory chain and RNA molecules involved in translation of these proteins inside mitochondria. Mitochondria have their own DNA and express their genes to produce proteins active in the electron transport chain. However, most of the proteins they need are encoded in the nucleus of the cell. They need to import most of their proteins to function.
Alterations in the DNA code, such as changing a letter, deleting a letter, inserting a letter or moving sections aroun proteins with abnormal functions.
If these abnormal functions cause the cell to grow, divide, ignore regulatory signals or assume new functions, cancers can develop
Fortunately, normal cells are good at repairing mistakes should they occur and have multiple systems for ensuring that the DNA co transmitted to its two daughter cells when it divides. Normal cells even have suicide programs if the mistakes are beyond repair, a p death, known as apoptosis. [Source: https://www.loxooncology.com/genomically-defined-cancers/genomic-alterations]
The study of nucleic acids began with the discovery of DNA, progressed to the study of genes and small fragments, and has now exploded to the field of genomics. Genomics is the study of entire genomes, including the complete set of genes, their nucleotide sequence and organization, and their interactions within a species and with other species. The advances in genomics have been made possible by DNA sequencing technology. [Source: https://opentextbc.ca/biology/chapter/10-3-genomics-and-proteomics/]
DNA cloning is the process of making multiple, identical copies of a particular piece of DNA. In a typical DNA cloning procedure, the gene or other DNA fragment of interest (perhaps a gene for a medically important human protein) is first inserted into a circular piece of DNA called a plasmid.- [https://www.khanacademy.org/science/...dna.../dna-cloning.../a/overview-dna-cloning]
DNA and RNA molecules are linear polymers built from individual units called nucleotides connected by bonds called phosphodiester linkages. DNA and RNA are used to store and pass genetic information from one generation to the next.
"The body maintains a balance of acids and bases in order to constantly maintain blood pH within a narrow range, despite the continuous generation of metabolic products. In turn, this allows the body to maintain cell enzyme systems in good operation conditions, together with the proper concentration of ionized (active) forms of various electrolytes such as Ca and Mg . This influences the speed of metabolic reactions and trans-membrane transportation systems (pharmacokinetics and pharmacodynamics)." - Luis Núñez Ochoa, Facultad de Medicina Veterinaria y Zootecnia, Unam, Mexico
Production Performance and Management Practices of Philippine Native Pigs in ...Garry D. Lasaga
Recently, there has been a proliferation of studies that deals with the major topic on the Conservation, Improvement and Profitable Utilization of the Philippine Native Pigs. One of the main reasons why there is an influx of research on native pigs is because there is a need to promote one of the government’s aim to the country, w/c is ultimately POVERTY ALLEVIATION.
African Swine Fever: Nature, Impacts and Threats to the Global Pig Industry Garry D. Lasaga
In August 2018, African Swine Fever (ASF), one of the world’s most feared swine infection made headlines as it hit for the first time ever, the world’s largest pig producer – China. This review paper summarizes the current state of knowledge and very recent updates on ASF.
Swine Production Performance Monitoring Data for 2014 - Dr. Arturo CaludGarry D. Lasaga
This is the 2014 Swine Production Performance Monitoring Data among participating commercial swine farms in the Philippines as provided by Dr. Arturo Calud.
Spermatogenesis in Domestic Animals - Dr. John J. ParrishGarry D. Lasaga
This presentation is an introduction to the principles of spermatogenesis of domestic animals by Dr. John J. Parrish of the University of Wisconsin-Madison (Animal Science Department).
StarCompliance is a leading firm specializing in the recovery of stolen cryptocurrency. Our comprehensive services are designed to assist individuals and organizations in navigating the complex process of fraud reporting, investigation, and fund recovery. We combine cutting-edge technology with expert legal support to provide a robust solution for victims of crypto theft.
Our Services Include:
Reporting to Tracking Authorities:
We immediately notify all relevant centralized exchanges (CEX), decentralized exchanges (DEX), and wallet providers about the stolen cryptocurrency. This ensures that the stolen assets are flagged as scam transactions, making it impossible for the thief to use them.
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We guide you through the process of filing a valid police report. Our support team provides detailed instructions on which police department to contact and helps you complete the necessary paperwork within the critical 72-hour window.
Launching the Refund Process:
Our team of experienced lawyers can initiate lawsuits on your behalf and represent you in various jurisdictions around the world. They work diligently to recover your stolen funds and ensure that justice is served.
At StarCompliance, we understand the urgency and stress involved in dealing with cryptocurrency theft. Our dedicated team works quickly and efficiently to provide you with the support and expertise needed to recover your assets. Trust us to be your partner in navigating the complexities of the crypto world and safeguarding your investments.
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Empowering the Data Analytics Ecosystem: A Laser Focus on Value
The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem:
1. Democratize Access, Not Data:
Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse.
Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources.
2. Foster Collaboration with Clear Roles:
Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities.
Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together.
3. Leverage Advanced Analytics Strategically:
AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis.
Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems.
4. Prioritize Data Quality with Automation:
Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues.
Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors.
5. Cultivate a Data-Driven Mindset:
Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making.
Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action.
Benefits of a Precise Ecosystem:
Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency.
Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights.
Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement.
Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation.
By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.
As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape.
In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity.
🔑 Key findings include:
🔍 Increased frequency and complexity of cyber threats.
🔍 Escalation of state-sponsored and criminally motivated cyber operations.
🔍 Active dark web exchanges of malicious tools and tactics.
Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities.
This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.
Levelwise PageRank with Loop-Based Dead End Handling Strategy : SHORT REPORT ...Subhajit Sahu
Abstract — Levelwise PageRank is an alternative method of PageRank computation which decomposes the input graph into a directed acyclic block-graph of strongly connected components, and processes them in topological order, one level at a time. This enables calculation for ranks in a distributed fashion without per-iteration communication, unlike the standard method where all vertices are processed in each iteration. It however comes with a precondition of the absence of dead ends in the input graph. Here, the native non-distributed performance of Levelwise PageRank was compared against Monolithic PageRank on a CPU as well as a GPU. To ensure a fair comparison, Monolithic PageRank was also performed on a graph where vertices were split by components. Results indicate that Levelwise PageRank is about as fast as Monolithic PageRank on the CPU, but quite a bit slower on the GPU. Slowdown on the GPU is likely caused by a large submission of small workloads, and expected to be non-issue when the computation is performed on massive graphs.
2. Syllabus
• Webpage: http://www.cse.unr.edu/~sushil/class/ai/
• Textbook: Russell and Norvig’s
Artificial Intelligence a Modern Approach, Third edition
• 40 % Assignments
• 40% Exams
• 20% Final Project
• Pairs encouraged
• Read the syllabus
• First assignment due Sept 11
3. Outline
• What is AI?
• A Brief History of AI
• What is the state of the Art
4. What is AI?
• AI seeks to understand and build intelligent entities
• AI is new
• AI coined in 1956 at Workshop at Dartmouth
• AI is hard
• But what is it?
5. Definitions
Thinking Humanly
The automation of activities that we
associate with human thinking…
(Haugeland)
Thinking Rationally
The study of the computations that
make it possible to perceive, reason,
and act (Wilson)
Acting Humanly
The study of how to make computers
do things at which, at the moment,
people are better (Rich and Knight)
Acting Rationally
AI is concerned with intelligent
behavior in artifacts (Nilsson)
Human performance metric Ideal or rational performance metric
6. Acting humanly – Turing
• Turing Test is an operational test for intelligent behavior (Turing, 1950)
• Turing predicted that by 2000, a machine might have a 30% chance of
fooling a lay person for 5 minutes
• Language, knowledge, reasoning, learning
• Natural language processing
• Knowledge representation
• Automatic reasoning
• Machine learning
• Total Turing test:
• Computer vision
• Robotics
7. Thinking humanly
• How do we answer how do we think?
• Introspection
• Experimentation – observing a person in action
• Brain imaging
• Once we know sufficiently precisely how we think , we can
write a computer program to do this
• This is Cognitive Science
• Distinct from AI but cross fertilization
8. Thinking rationally
• Socrates is a man, All men are mortal, Therefore Socrates is
mortal
• Logic and derivation rules
• Once you have Facts, and a set of rules for manipulating facts,
you can (automatically) derive conclusions (prove theorems)
• We will study logic and the limits of theorem proving
9. Acting Rationally
• Rational behavior: doing the right thing
• Maximize goal achievement given the available information
• An agent is just something that acts
• Doesn’t necessarily involve “thinking rationally”
• Hot stove reflex is not the effect of a logical sequence of rule
applications that deduce the optimal action is to move hand
away from stove
10. Rational Agents
• An agent is an entity that perceives and acts
• F(P*) Action
• For any given class of environments and tasks, we seek the
agent (or class of agents) with the best performance
• Perfect rationality is computationally intractable
• So we design the best program for given machine resources
11. Foundations and History
• Philosophy
• Logic, methods of reasoning, foundations of learning, language, rationality
• Mathematics
• Formal representations and proof. Algorithms, computation, decidability, tractability,
probability
• Economics
• Rational agents maximize profits (payoff), OR
• Psychology
• Adaptation, learning, Experimental techniques
• Neuroscience
• Neural nets, when will computers reach human level computing capacity
• Control Theory
• Homeostatic systems, agents maximize an objective function, agents minimize error
between goals state and current state
12. History
• 1942: Boolean circuit model of
the brain
• 1950: Turing
• 1950s:
• Samuel: Checkers
• Newell and Simon: Logic Theorist
• Gelernter: Geometry engine
• 1956: Dartmouth Meeting. The
term: Artificial Intelligence coined
• 50s-60s: Everyone: Cannot do X.
AI: Here’s a program for X. Lisp
invented
• Mid 60s: Computational
Complexity kills scaling up in AI
• 70s: Expert systems
• 80s+: Industrial Expert systems
• 90s: AI winter + Neural Nets,
GAs, NNs, Fuzzy logic
• 90s: Agents
• 2003+: Human level
competitiveness with very large
data sets
15. State of the art
• Speech recognition
• United Airlines’ speech recognition system for support, booking
• Siri,
• Planning and Scheduling
• Spacecraft ops (Nasa’s rovers)
• Games
• Deep blue and chess. Humans are no longer competitive
• Spam fighting
• 80 – 90 % filtered out
• Logistics
• DART generated plans in hours that would have taken weeks
• DARPA stated that this single application paid back DARPA’s 30 year investment in AI
• Machine Translation: Google translate?