In this presentation, Sudhanshu introduces IoT and associated trends. Sudhanshu is interested in IoT applications in wireless sensor networks which he wants to use to build an application that tracks eating habits and suggests workouts.
In this presentation, Sudhanshu introduces IoT and associated trends. Sudhanshu is interested in IoT applications in wireless sensor networks which he wants to use to build an application that tracks eating habits and suggests workouts.
AI - developing a broad portfolio of educational activitiesSonja Aits
A presentation of different types of education activities that can build competence in artificial intelligence for different target audiences, e.g. children, university students, professionals. Presented at a conference on pedagogy and higher education teaching at Lund University 2022-11-17
I gave this short presentation at the TIVIT Next Media planning workshop on Tuesday 17th of May 2011 to outline some ideas on what to focus on when researching Open Data.
This is very much work in progress - ideas and comments are welcomed!
Presentations, exercises and discussion of the following topics:
- General requirements of research Data management
- Guidelines and responsibilities
- Data management plans (DMP) for the Swiss national Science Foundation (SNSF)
- Data management in practice
- Prerequisites for re-use
- Useful services and tools
- Exchange of experiences, methods and tools
Data; Data manipulation, sorting, grouping, rearranging. Plotting the data. D...jybufgofasfbkpoovh
Data; Data manipulation, sorting, grouping, rearranging. Plotting the data. Descriptive statistics. Inferential statistics. Python Libraries for Data Science.
Twitter, interpretation and math: New interdisciplinary approaches to large-scale analysis of the digital media landscape
Higher seminar at Media and Communication Studies at the School of Culture and Education, Södertörn University with Mattias Östmar, independent data scientist.
The title of this seminar is "Twitter, interpretation and math: New interdisciplinary approaches to large-scale analysis of the digital media landscape".
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
More Related Content
Similar to Forget about the Terminator Already: AI Education for All
AI - developing a broad portfolio of educational activitiesSonja Aits
A presentation of different types of education activities that can build competence in artificial intelligence for different target audiences, e.g. children, university students, professionals. Presented at a conference on pedagogy and higher education teaching at Lund University 2022-11-17
I gave this short presentation at the TIVIT Next Media planning workshop on Tuesday 17th of May 2011 to outline some ideas on what to focus on when researching Open Data.
This is very much work in progress - ideas and comments are welcomed!
Presentations, exercises and discussion of the following topics:
- General requirements of research Data management
- Guidelines and responsibilities
- Data management plans (DMP) for the Swiss national Science Foundation (SNSF)
- Data management in practice
- Prerequisites for re-use
- Useful services and tools
- Exchange of experiences, methods and tools
Data; Data manipulation, sorting, grouping, rearranging. Plotting the data. D...jybufgofasfbkpoovh
Data; Data manipulation, sorting, grouping, rearranging. Plotting the data. Descriptive statistics. Inferential statistics. Python Libraries for Data Science.
Twitter, interpretation and math: New interdisciplinary approaches to large-scale analysis of the digital media landscape
Higher seminar at Media and Communication Studies at the School of Culture and Education, Södertörn University with Mattias Östmar, independent data scientist.
The title of this seminar is "Twitter, interpretation and math: New interdisciplinary approaches to large-scale analysis of the digital media landscape".
Toxic effects of heavy metals : Lead and Arsenicsanjana502982
Heavy metals are naturally occuring metallic chemical elements that have relatively high density, and are toxic at even low concentrations. All toxic metals are termed as heavy metals irrespective of their atomic mass and density, eg. arsenic, lead, mercury, cadmium, thallium, chromium, etc.
hematic appreciation test is a psychological assessment tool used to measure an individual's appreciation and understanding of specific themes or topics. This test helps to evaluate an individual's ability to connect different ideas and concepts within a given theme, as well as their overall comprehension and interpretation skills. The results of the test can provide valuable insights into an individual's cognitive abilities, creativity, and critical thinking skills
Phenomics assisted breeding in crop improvementIshaGoswami9
As the population is increasing and will reach about 9 billion upto 2050. Also due to climate change, it is difficult to meet the food requirement of such a large population. Facing the challenges presented by resource shortages, climate
change, and increasing global population, crop yield and quality need to be improved in a sustainable way over the coming decades. Genetic improvement by breeding is the best way to increase crop productivity. With the rapid progression of functional
genomics, an increasing number of crop genomes have been sequenced and dozens of genes influencing key agronomic traits have been identified. However, current genome sequence information has not been adequately exploited for understanding
the complex characteristics of multiple gene, owing to a lack of crop phenotypic data. Efficient, automatic, and accurate technologies and platforms that can capture phenotypic data that can
be linked to genomics information for crop improvement at all growth stages have become as important as genotyping. Thus,
high-throughput phenotyping has become the major bottleneck restricting crop breeding. Plant phenomics has been defined as the high-throughput, accurate acquisition and analysis of multi-dimensional phenotypes
during crop growing stages at the organism level, including the cell, tissue, organ, individual plant, plot, and field levels. With the rapid development of novel sensors, imaging technology,
and analysis methods, numerous infrastructure platforms have been developed for phenotyping.
ESR spectroscopy in liquid food and beverages.pptxPRIYANKA PATEL
With increasing population, people need to rely on packaged food stuffs. Packaging of food materials requires the preservation of food. There are various methods for the treatment of food to preserve them and irradiation treatment of food is one of them. It is the most common and the most harmless method for the food preservation as it does not alter the necessary micronutrients of food materials. Although irradiated food doesn’t cause any harm to the human health but still the quality assessment of food is required to provide consumers with necessary information about the food. ESR spectroscopy is the most sophisticated way to investigate the quality of the food and the free radicals induced during the processing of the food. ESR spin trapping technique is useful for the detection of highly unstable radicals in the food. The antioxidant capability of liquid food and beverages in mainly performed by spin trapping technique.
Seminar of U.V. Spectroscopy by SAMIR PANDASAMIR PANDA
Spectroscopy is a branch of science dealing the study of interaction of electromagnetic radiation with matter.
Ultraviolet-visible spectroscopy refers to absorption spectroscopy or reflect spectroscopy in the UV-VIS spectral region.
Ultraviolet-visible spectroscopy is an analytical method that can measure the amount of light received by the analyte.
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.
The ability to recreate computational results with minimal effort and actionable metrics provides a solid foundation for scientific research and software development. When people can replicate an analysis at the touch of a button using open-source software, open data, and methods to assess and compare proposals, it significantly eases verification of results, engagement with a diverse range of contributors, and progress. However, we have yet to fully achieve this; there are still many sociotechnical frictions.
Inspired by David Donoho's vision, this talk aims to revisit the three crucial pillars of frictionless reproducibility (data sharing, code sharing, and competitive challenges) with the perspective of deep software variability.
Our observation is that multiple layers — hardware, operating systems, third-party libraries, software versions, input data, compile-time options, and parameters — are subject to variability that exacerbates frictions but is also essential for achieving robust, generalizable results and fostering innovation. I will first review the literature, providing evidence of how the complex variability interactions across these layers affect qualitative and quantitative software properties, thereby complicating the reproduction and replication of scientific studies in various fields.
I will then present some software engineering and AI techniques that can support the strategic exploration of variability spaces. These include the use of abstractions and models (e.g., feature models), sampling strategies (e.g., uniform, random), cost-effective measurements (e.g., incremental build of software configurations), and dimensionality reduction methods (e.g., transfer learning, feature selection, software debloating).
I will finally argue that deep variability is both the problem and solution of frictionless reproducibility, calling the software science community to develop new methods and tools to manage variability and foster reproducibility in software systems.
Exposé invité Journées Nationales du GDR GPL 2024
2. Matemaattis-luonnontieteellinen tiedekunta
WHY DO WE THINK
AI = TERMINATOR?
FOUR REASONS
1. Science fiction
2. “Uncanny valley” (i.e., psychological
aversion to almost but not quite
humanlike forms)
3. Click-bait media
4. Fear of the unknown
13.12.2017AI Day / Teemu Roos 2
icl_researchteemu_roos
3. Matemaattis-luonnontieteellinen tiedekunta
1. Societal cost: Fear of AI slows down the adoption of AI solutions
2. Scientific cost: Without research funding, no world-class science
3. Industrial cost: No world-class science, no innovations
13.12.2017AI Day / Teemu Roos 3
WHY WE NEED TO CHANGE THIS
icl_researchteemu_roos
4. Matemaattis-luonnontieteellinen tiedekunta
HOW WE CAN CHANGE THIS
• Experts should reach out more to the
public about their work
• Journalists should dump their click-bait
headlines
• We should all be more critical about what
we read and see
• Most importantly: We need AI education on
all levels, free for all
13.12.2017AI Day / Teemu Roos 4
icl_researchteemu_roos
5. Matemaattis-luonnontieteellinen tiedekunta
• Exponential is not enough: Exponential progress (speed) is trumped by exponential
increase in problem hardness
• Think of the self-improving system: yourself!
• Narrow AI: Even though computers beat humans in certain tasks (arithmetics, chess,
Go, ...), they are very stupid in many other ways
• There is no “brain-in-a-jar” AI that learns new skills: it’s always a different system.
13.12.2017AI Day / Teemu Roos 5
LESSON 1:
WHY THE TERMINATOR WILL NOT COME
icl_researchteemu_roos
6. Matemaattis-luonnontieteellinen tiedekunta
1. University education: already strong and growing
• e.g., AI in Games, 2 x Deep Learning, Philosophy of AI, AI and the Law, Data Science
2. Professional training: AI Diploma training starting in Fall 2018
• both technical and non-technical (“CEO”) tracks, 12 days
• plus: separate events, e.g., “AI:n perusteet” (in Finnish) March 8-9, 2018
• internships (academia ⟺ industry), thesis supervision, hackathons
3. Schools
• teacher education, curriculum reforms
4. Open education for all
• Open Universities (University of Helsinki & Aalto), e.g., AI course starts Jan 4th, 2018
• MOOCs
13.12.2017AI Day / Teemu Roos 6
FCAI AI & EDUCATION PROGRAM
icl_researchteemu_roos
7. Matemaattis-luonnontieteellinen tiedekunta
PART I: ELEMENTS OF AI
• Starting in May 2018
• three weeks, 2 credit units
• philosophy and history of AI
• basic concepts: search and planning,
games, machine learning, neural networks,
signal processing, robotics
• “AI literacy”
• no programming or maths skills required
• free of charge
13.12.2017AI Day / Teemu Roos 7
icl_researchteemu_roos
8. Matemaattis-luonnontieteellinen tiedekunta
PART II: AI PROGRAMMING
• Starting in Winter 2018-2019
• 3 credit units
• theory and practice
• same themes as in Elements of AI
• path to becoming an AI developer
• programming (Java or python) and maths
required
• goal: worlds’s best AI MOOCs
13.12.2017AI Day / Teemu Roos 8
icl_researchteemu_roos