Artificial intelligence (AI) is software that allows computers and robots to perform tasks in a way that mimics human intelligence. John McCarthy first proposed the term "artificial intelligence" in 1956. AI uses techniques like machine learning, natural language processing, and computer vision to perform tasks previously only done by humans, such as playing games, recognizing speech, and understanding language. While AI has advantages like efficiency, reliability, and ability to handle complex tasks, it also has drawbacks like limited ability and lack of complete human traits. The ultimate goal of AI research is to solve problems humans cannot.
Here is a small presentation about Artificial Intelligence.
How ai system works and how Artificial Intelligence works in our day to day life to solve real life problems.
I hope you will like it.
Artificial intelligence (AI) is the intelligence of machines and robots and the branch of computer science that aims to create it
the ability to solve problems
the ability to act rationally
the ability to act like humans
Here is a small presentation about Artificial Intelligence.
How ai system works and how Artificial Intelligence works in our day to day life to solve real life problems.
I hope you will like it.
Artificial intelligence (AI) is the intelligence of machines and robots and the branch of computer science that aims to create it
the ability to solve problems
the ability to act rationally
the ability to act like humans
In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is, this being a good thing to decide before embarking.
Artificial intelligence is purported to improve workplace productivity. But does it really? This presentation takes a hard look at where AI is today and which aspects can truly help today's Digital Workplace improve productivity.
What is AI and how it works? What is early history of AI. what are risks and benefits of AI? Current status and future of AI. General perceptions about AI. Achievement of AI. Will AI be more beneficent or more destructive?
Artificial intelligence or AI in short is the latest technology on which the whole world is working today. We at myassignmenthelp.net are providing help with all the assignments and projects. So when ever you need help with any work related to AI feel free to get in touch
machines will be capable, within 20 years, of doing any work a man can do." Two years later, MIT researcher Marvin Minsky predicted, "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."
(artificial intelligence innovator Herbert Simon.1965
Hello beautiful people, i hope you all are doing great. Here I'm sharing a short PPT on Artificial Intelligence. if you found it helpful. say thanks it's appreciated.
Pattern Recognition is the branch of machine learning a computer science which deals with the regularities and patterns in the data that can further be used to classify and categorize the data with the help of Pattern Recognition System.
“The assignment of a physical object or event to one of several pre-specified categories”-- Duda & Hart
Pattern Recognition System is responsible for generating patterns and similarities among given problem/data space, that can further be used to generate solutions to complex problems effectively and efficiently.
Certain problems that can be solved by humans, can also be made to be solved by machine by using this process.
Introduction to Artificial Intelligence is the part of IOE Computer Engineering Syllabus covering the first chapter of AI. It covers definition, types and characteristics of AI. Similarly, it also deals with the Turing test for determining machine intelligence.
Artificial intelligence training in hyderabadArjun_Raghu
Integrum Litera providing Artificial Intelligence Training in Hyderabad.Get familiar with the most trending technologies on the planet through Integrum Litera’s most exclusive and technology-infused certification programs in Data Science, Data Analytics, Machine Learning, Neural Networks, Deep learning, Robotics & Automation technologies.
In which we try to explain why we consider artificial intelligence to be a subject most worthy of study, and in which we try to decide what exactly it is, this being a good thing to decide before embarking.
Artificial intelligence is purported to improve workplace productivity. But does it really? This presentation takes a hard look at where AI is today and which aspects can truly help today's Digital Workplace improve productivity.
What is AI and how it works? What is early history of AI. what are risks and benefits of AI? Current status and future of AI. General perceptions about AI. Achievement of AI. Will AI be more beneficent or more destructive?
Artificial intelligence or AI in short is the latest technology on which the whole world is working today. We at myassignmenthelp.net are providing help with all the assignments and projects. So when ever you need help with any work related to AI feel free to get in touch
machines will be capable, within 20 years, of doing any work a man can do." Two years later, MIT researcher Marvin Minsky predicted, "Within a generation ... the problem of creating 'artificial intelligence' will substantially be solved."
(artificial intelligence innovator Herbert Simon.1965
Hello beautiful people, i hope you all are doing great. Here I'm sharing a short PPT on Artificial Intelligence. if you found it helpful. say thanks it's appreciated.
Pattern Recognition is the branch of machine learning a computer science which deals with the regularities and patterns in the data that can further be used to classify and categorize the data with the help of Pattern Recognition System.
“The assignment of a physical object or event to one of several pre-specified categories”-- Duda & Hart
Pattern Recognition System is responsible for generating patterns and similarities among given problem/data space, that can further be used to generate solutions to complex problems effectively and efficiently.
Certain problems that can be solved by humans, can also be made to be solved by machine by using this process.
Introduction to Artificial Intelligence is the part of IOE Computer Engineering Syllabus covering the first chapter of AI. It covers definition, types and characteristics of AI. Similarly, it also deals with the Turing test for determining machine intelligence.
Artificial intelligence training in hyderabadArjun_Raghu
Integrum Litera providing Artificial Intelligence Training in Hyderabad.Get familiar with the most trending technologies on the planet through Integrum Litera’s most exclusive and technology-infused certification programs in Data Science, Data Analytics, Machine Learning, Neural Networks, Deep learning, Robotics & Automation technologies.
PowerPoint Presentation on the topic "Artificial Intelligence" including the brief history,information about the founders and pioneers of the concept and the varied applications and future of Artificial Intelligence.
Artificial Intelligence an Amazing presentation By Group4.
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Intelligence: “The capacity to learn and solve problems.”
Artificial intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.
Till now we have discussed in brief about Artificial Intelligence.
We have discussed some of its principles, its applications, its achievements etc.
The ultimate goal of institutions and scientists working of AI is to solve majority of the problems or to achieve the tasks which we humans directly can’t accomplish.
It is for sure that development in this field of computer science will change the complete scenario of the world. Now it is the responsibility of creamy layer of engineers to develop this field.
Deep Behavioral Phenotyping in Systems Neuroscience for Functional Atlasing a...Ana Luísa Pinho
Functional Magnetic Resonance Imaging (fMRI) provides means to characterize brain activations in response to behavior. However, cognitive neuroscience has been limited to group-level effects referring to the performance of specific tasks. To obtain the functional profile of elementary cognitive mechanisms, the combination of brain responses to many tasks is required. Yet, to date, both structural atlases and parcellation-based activations do not fully account for cognitive function and still present several limitations. Further, they do not adapt overall to individual characteristics. In this talk, I will give an account of deep-behavioral phenotyping strategies, namely data-driven methods in large task-fMRI datasets, to optimize functional brain-data collection and improve inference of effects-of-interest related to mental processes. Key to this approach is the employment of fast multi-functional paradigms rich on features that can be well parametrized and, consequently, facilitate the creation of psycho-physiological constructs to be modelled with imaging data. Particular emphasis will be given to music stimuli when studying high-order cognitive mechanisms, due to their ecological nature and quality to enable complex behavior compounded by discrete entities. I will also discuss how deep-behavioral phenotyping and individualized models applied to neuroimaging data can better account for the subject-specific organization of domain-general cognitive systems in the human brain. Finally, the accumulation of functional brain signatures brings the possibility to clarify relationships among tasks and create a univocal link between brain systems and mental functions through: (1) the development of ontologies proposing an organization of cognitive processes; and (2) brain-network taxonomies describing functional specialization. To this end, tools to improve commensurability in cognitive science are necessary, such as public repositories, ontology-based platforms and automated meta-analysis tools. I will thus discuss some brain-atlasing resources currently under development, and their applicability in cognitive as well as clinical neuroscience.
(May 29th, 2024) Advancements in Intravital Microscopy- Insights for Preclini...Scintica Instrumentation
Intravital microscopy (IVM) is a powerful tool utilized to study cellular behavior over time and space in vivo. Much of our understanding of cell biology has been accomplished using various in vitro and ex vivo methods; however, these studies do not necessarily reflect the natural dynamics of biological processes. Unlike traditional cell culture or fixed tissue imaging, IVM allows for the ultra-fast high-resolution imaging of cellular processes over time and space and were studied in its natural environment. Real-time visualization of biological processes in the context of an intact organism helps maintain physiological relevance and provide insights into the progression of disease, response to treatments or developmental processes.
In this webinar we give an overview of advanced applications of the IVM system in preclinical research. IVIM technology is a provider of all-in-one intravital microscopy systems and solutions optimized for in vivo imaging of live animal models at sub-micron resolution. The system’s unique features and user-friendly software enables researchers to probe fast dynamic biological processes such as immune cell tracking, cell-cell interaction as well as vascularization and tumor metastasis with exceptional detail. This webinar will also give an overview of IVM being utilized in drug development, offering a view into the intricate interaction between drugs/nanoparticles and tissues in vivo and allows for the evaluation of therapeutic intervention in a variety of tissues and organs. This interdisciplinary collaboration continues to drive the advancements of novel therapeutic strategies.
A brief information about the SCOP protein database used in bioinformatics.
The Structural Classification of Proteins (SCOP) database is a comprehensive and authoritative resource for the structural and evolutionary relationships of proteins. It provides a detailed and curated classification of protein structures, grouping them into families, superfamilies, and folds based on their structural and sequence similarities.
Nutraceutical market, scope and growth: Herbal drug technologyLokesh Patil
As consumer awareness of health and wellness rises, the nutraceutical market—which includes goods like functional meals, drinks, and dietary supplements that provide health advantages beyond basic nutrition—is growing significantly. As healthcare expenses rise, the population ages, and people want natural and preventative health solutions more and more, this industry is increasing quickly. Further driving market expansion are product formulation innovations and the use of cutting-edge technology for customized nutrition. With its worldwide reach, the nutraceutical industry is expected to keep growing and provide significant chances for research and investment in a number of categories, including vitamins, minerals, probiotics, and herbal supplements.
Richard's entangled aventures in wonderlandRichard Gill
Since the loophole-free Bell experiments of 2020 and the Nobel prizes in physics of 2022, critics of Bell's work have retreated to the fortress of super-determinism. Now, super-determinism is a derogatory word - it just means "determinism". Palmer, Hance and Hossenfelder argue that quantum mechanics and determinism are not incompatible, using a sophisticated mathematical construction based on a subtle thinning of allowed states and measurements in quantum mechanics, such that what is left appears to make Bell's argument fail, without altering the empirical predictions of quantum mechanics. I think however that it is a smoke screen, and the slogan "lost in math" comes to my mind. I will discuss some other recent disproofs of Bell's theorem using the language of causality based on causal graphs. Causal thinking is also central to law and justice. I will mention surprising connections to my work on serial killer nurse cases, in particular the Dutch case of Lucia de Berk and the current UK case of Lucy Letby.
Earliest Galaxies in the JADES Origins Field: Luminosity Function and Cosmic ...Sérgio Sacani
We characterize the earliest galaxy population in the JADES Origins Field (JOF), the deepest
imaging field observed with JWST. We make use of the ancillary Hubble optical images (5 filters
spanning 0.4−0.9µm) and novel JWST images with 14 filters spanning 0.8−5µm, including 7 mediumband filters, and reaching total exposure times of up to 46 hours per filter. We combine all our data
at > 2.3µm to construct an ultradeep image, reaching as deep as ≈ 31.4 AB mag in the stack and
30.3-31.0 AB mag (5σ, r = 0.1” circular aperture) in individual filters. We measure photometric
redshifts and use robust selection criteria to identify a sample of eight galaxy candidates at redshifts
z = 11.5 − 15. These objects show compact half-light radii of R1/2 ∼ 50 − 200pc, stellar masses of
M⋆ ∼ 107−108M⊙, and star-formation rates of SFR ∼ 0.1−1 M⊙ yr−1
. Our search finds no candidates
at 15 < z < 20, placing upper limits at these redshifts. We develop a forward modeling approach to
infer the properties of the evolving luminosity function without binning in redshift or luminosity that
marginalizes over the photometric redshift uncertainty of our candidate galaxies and incorporates the
impact of non-detections. We find a z = 12 luminosity function in good agreement with prior results,
and that the luminosity function normalization and UV luminosity density decline by a factor of ∼ 2.5
from z = 12 to z = 14. We discuss the possible implications of our results in the context of theoretical
models for evolution of the dark matter halo mass function.
Introduction:
RNA interference (RNAi) or Post-Transcriptional Gene Silencing (PTGS) is an important biological process for modulating eukaryotic gene expression.
It is highly conserved process of posttranscriptional gene silencing by which double stranded RNA (dsRNA) causes sequence-specific degradation of mRNA sequences.
dsRNA-induced gene silencing (RNAi) is reported in a wide range of eukaryotes ranging from worms, insects, mammals and plants.
This process mediates resistance to both endogenous parasitic and exogenous pathogenic nucleic acids, and regulates the expression of protein-coding genes.
What are small ncRNAs?
micro RNA (miRNA)
short interfering RNA (siRNA)
Properties of small non-coding RNA:
Involved in silencing mRNA transcripts.
Called “small” because they are usually only about 21-24 nucleotides long.
Synthesized by first cutting up longer precursor sequences (like the 61nt one that Lee discovered).
Silence an mRNA by base pairing with some sequence on the mRNA.
Discovery of siRNA?
The first small RNA:
In 1993 Rosalind Lee (Victor Ambros lab) was studying a non- coding gene in C. elegans, lin-4, that was involved in silencing of another gene, lin-14, at the appropriate time in the
development of the worm C. elegans.
Two small transcripts of lin-4 (22nt and 61nt) were found to be complementary to a sequence in the 3' UTR of lin-14.
Because lin-4 encoded no protein, she deduced that it must be these transcripts that are causing the silencing by RNA-RNA interactions.
Types of RNAi ( non coding RNA)
MiRNA
Length (23-25 nt)
Trans acting
Binds with target MRNA in mismatch
Translation inhibition
Si RNA
Length 21 nt.
Cis acting
Bind with target Mrna in perfect complementary sequence
Piwi-RNA
Length ; 25 to 36 nt.
Expressed in Germ Cells
Regulates trnasposomes activity
MECHANISM OF RNAI:
First the double-stranded RNA teams up with a protein complex named Dicer, which cuts the long RNA into short pieces.
Then another protein complex called RISC (RNA-induced silencing complex) discards one of the two RNA strands.
The RISC-docked, single-stranded RNA then pairs with the homologous mRNA and destroys it.
THE RISC COMPLEX:
RISC is large(>500kD) RNA multi- protein Binding complex which triggers MRNA degradation in response to MRNA
Unwinding of double stranded Si RNA by ATP independent Helicase
Active component of RISC is Ago proteins( ENDONUCLEASE) which cleave target MRNA.
DICER: endonuclease (RNase Family III)
Argonaute: Central Component of the RNA-Induced Silencing Complex (RISC)
One strand of the dsRNA produced by Dicer is retained in the RISC complex in association with Argonaute
ARGONAUTE PROTEIN :
1.PAZ(PIWI/Argonaute/ Zwille)- Recognition of target MRNA
2.PIWI (p-element induced wimpy Testis)- breaks Phosphodiester bond of mRNA.)RNAse H activity.
MiRNA:
The Double-stranded RNAs are naturally produced in eukaryotic cells during development, and they have a key role in regulating gene expression .
This pdf is about the Schizophrenia.
For more details visit on YouTube; @SELF-EXPLANATORY;
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3. History
• John McCarthy proposed
the term “Artificial
Intelligence” in 1956 in a
conference at Dartmouth
college
4. What is Artificial Intelligence?
• Software technologies that make a computer or robot perform
equal to or better than normal human computational ability in
accuracy, capacity, and speed.
5. How is Artificial Intelligence different?
Artificial Intelligence Natural Intelligence (human Intelligence)
• Non creative • Creative
• Precise • May contain error (human error)
• Consistent • Non consistent
• Multitasking • Can’t handle multiple tasks all the time
6. Cognitive technologies
• Cognitive technologies are products of the field of artificial intelligence.
They are able to perform tasks that only humans used to be able to do.
• Examples of cognitive technologies include computer vision, machine
learning, natural language processing, speech recognition, and robotics.
8. Applications
• Data mining
• Robotics
• Roomba
• ASIMO
• Game playing
• Speech recognition
• Understanding natural language
• Expert systems( e.g. MYCIN)
• Used in philosophy, mathematics , statistics , economics, computer engineering and
linguistics
9. All these applications rely on:
• Search & Optimization
• Knowledge representation
• Learning
• Planning
11. Advantages
• It can help improve our way of life
• Machines will be able to do jobs that require detailed instructions
• Mental alertness and decision making capabilities
• Use robots for heavy construction, military benefits, or even for personal
assistance at private homes
• There will be less injuries and stress to human beings
• UsingArtificial intelligence (AI) help instructional designers to provide creative
solutions , problem solving strategies and more interactivity in the learning .
12. Drawbacks
• Lacking complete traits of human intelligence
• Limited ability
• Slow real time response
• High cost
13. • Scientists have been using AI to test theories and notions about how our
brains work
• AI opens up new and exciting avenues for entertainment possibilities.
• AI also makes interactive electronic games more fun by making the
computer controlled characters more realistic and human-like.
15. Conclusion
• The ultimate goal of institutions and scientists working on
artificial intelligence is to solve majority of the problems or to
achieve the tasks which we (humans) directly can’t accomplish,It
is for sure that development in this field will change the complete
scenario of the world.