#demystifing Ai

[algoworks]

What if you could eliminate inefficiencies, uncover meaningful insights, and build stronger customer connections—all without overhauling your operations?

Everyday AI by Algoworks delivers scalable, actionable solutions that fit into your routine and transform your business outcomes. 

Ready to make AI work for you? Let’s explore the possibilities together. 

[kickthecanrevolution]

I got up yesterday and headed to a cafe on Ocean Beach and worked on my business plan. Halfway through, I realized how bored I was with it - it felt generic and old. I started doubting myself, what do I really have to offer? I’ve spent the last 15 years putting out fires - can I even design anything anymore? Do I even have any skills other than pure survival? I felt exhausted and demoralized.

I gathered up my things and stopped by a garden store to pick up some early girl tomatoes to plant. As I started my engine up to go home, my friend D texted, asking if I wanted to stop by. She lives close. I had a whole excuse written out why I couldn’t go, erased it and said “that would be great”. I can’t be so isolated anymore, I have got to fight those urges of fleeing others when I’m in a dark place. We heal in community, not always alone.

We talked it all through. She is incredible and gave me great advice. I’m fascinated by how the metaverse and digital twins could be used in learning and training. There’s some demystification to be done in the world of AI - there’s a lot to be concerned about and a lot to be excited about. It’s like the very beginning of internet shopping, no one in their right minds would put their credit cards online. There were some good reasons for that and also a lot of imagined fears due to it being so new. Having a way of recommending AI in addition to more human-centered learning is the future. I’ve got the human centered part down, I just need the AI education. She showed me where to get it.

We ended up going to dinner where I ran into another friend who lives in the neighborhood. I felt so much better about the direction I could take, even though it will be a lot harder. It feels more promising. Most of all, I’m just proud of myself for saying yes in that moment.

[robhorninginternalexile]

the whole world into one book

Another long excerpt from The Order of Things that speaks to the same point — that the focus on language as an independent, overriding entity in structuralism has returned in AI development, catering perhaps to the same fantasy of escaping the "unhappy consciousness":

Language did not return into the field of thought directly and in its own right until the end of the nineteenth century. We might even have said until the twentieth, had not Nietzsche the philologist — and even in that field he was so wise, he knew so much, he wrote such good books — been the first to connect the philosophical task with a radical reflection upon language. And now, in this philosophical–philological space opened up for us by Nietzsche, language wells up in an enigmatic multiplicity that must be mastered. There appear, like so many projects (or chimeras, who can tell as yet?), the themes of a universal formalization of all discourse, or the themes of an integral exegesis of the world which would at the same time be its total demystification, or those of a general theory of signs; or again, the theme (historically probably the first) of a transformation without residuum, of a total reabsorption of all forms of discourse into a single word, of all books into a single page, of the whole world into one book. The great task to which Mallarmé dedicated himself, right up to his death, is the one that dominates us now; in its stammerings, it embraces all our current efforts to confine the fragmented being of language once more within a perhaps impossible unity .... To the Nietzschean question: ‘Who is speaking?’, Mallarmé replies – and constantly reverts to that reply – by saying that what is speaking is, in its solitude, in its fragile vibration, in its nothingness, the word itself – not the meaning of the word, but its enigmatic and precarious being ... It is quite possible that all those questions now confronting our curiosity (What is language? What is a sign? What is unspoken in the world, in our gestures, in the whole enigmatic heraldry of our behaviour, our dreams, our sicknesses – does all that speak, and if so in what language and in obedience to what grammar? Is everything significant, and, if not, what is, and for whom, and in accordance with what rules? What relation is there between language and being, and is it really to being that language is always addressed – at least, language that speaks truly? What, then, is this language that says nothing, is never silent, and is called ‘literature’?) — it is quite possible that all these questions are presented today in the distance that was never crossed between Nietzsche’s question and Mallarmé’s reply... The whole curiosity of our thought now resides in the question: What is language, how can we find a way round it in order to make it appear in itself, in all its plenitude? ... To discover the vast play of language contained once more within a single space might be just as decisive a leap towards a wholly new form of thought as to draw to a close a mode of knowing constituted during the previous century.

[starbuckai]

Ask AI for its reasoning systems

Honoria: I am trying to help my college students work with AI as collaborators, especially in brainstorming & ideation phases of creative work. Do you advise students to routinely ask the AI about the reasoning systems it is basing advice and ideas upon?

Gemini: That's a fantastic and forward-thinking approach to integrating AI into creative education! Yes, I absolutely advise students to routinely ask the AI about the reasoning systems it is basing its advice and ideas upon. This practice fosters several crucial skills and a deeper understanding of AI as a tool:

Here's why it's so important:

Demystifying the "Black Box": AI can often feel like a magical entity dispensing wisdom. By asking about its reasoning, students begin to understand that AI operates based on algorithms, data, and patterns. This demystification is crucial for moving beyond simply accepting AI output and towards critical engagement.  

Understanding Limitations and Biases: Knowing the underlying systems can reveal potential limitations and biases in the AI's suggestions. For example, if an AI is trained primarily on Western art history, its brainstorming might be skewed. Understanding this helps students contextualize the AI's input and avoid blindly adopting potentially narrow perspectives.

Developing Critical Thinking Skills: Asking "why?" encourages students to apply critical thinking to the AI's contributions. They learn to evaluate the rationale behind suggestions, identify assumptions, and determine the relevance and usefulness of the AI's input for their specific creative goals.  

Fostering a Collaborative Mindset: By understanding the AI's "thinking," students can engage in a more informed dialogue. Instead of just receiving ideas, they can challenge assumptions, ask for alternative perspectives based on different reasoning, and guide the AI towards more fruitful avenues of exploration. This truly transforms the interaction into a collaboration.

Learning About AI as a Tool: This practice provides students with valuable insights into how AI works, its strengths, and its weaknesses. This knowledge is increasingly important for anyone working in creative fields, as AI tools become more prevalent. It empowers them to be informed users and even potential future developers of such technologies.

Encouraging Experimentation: Understanding the reasoning can inspire students to experiment with different prompts and parameters to see how they influence the AI's output. They can learn to "steer" the AI more effectively by understanding its underlying logic.

How students can ask about reasoning:

Directly ask: "Could you explain the reasoning behind these brainstorming ideas?" or "What data or principles are you drawing upon to suggest this approach?"

Request alternatives based on different reasoning: "Can you suggest some alternative ideas based on a different aesthetic principle or a different cultural context?"

Challenge assumptions: "You suggested this color palette. What are the underlying associations or theories that inform this suggestion? Are there other possibilities based on different associations?"

Inquire about the training data (if the AI can provide general information): "Can you tell me generally about the types of data you were trained on that might influence your suggestions in this area?"

By encouraging this kind of inquisitive interaction, you are not just teaching students how to use AI, but how to think with AI in a critical, informed, and truly collaborative way. This will be invaluable as they navigate the evolving landscape of creative work.

[bofeiham]

Psychophobia: A Structural Analysis of Societal Fear and Systemic Discrimination Against Individuals with Mental Disorders

I. Conceptual Framework of Psychophobia

A. Definition and Theoretical Basis

1. Operational definition: Pathological fear/aversion towards psychiatric conditions and affected individuals

2. Comparative analysis with related concepts (stigmatization vs. phobic response)

3. Historical evolution from medieval "madhouse" concepts to modern diagnostic labels

B. Epidemiological Data and Sociodemographic Patterns

1. Global prevalence statistics (WHO 2023 report: 68% nations report systemic discrimination)

2. Cross-cultural manifestations: Comparative study of Eastern vs. Western societies

3. High-risk demographics: Analysis of double discrimination against minority mental health patients

II. Mechanisms of Stigmatization

A. Media-driven Stereotype Formation

1. Content analysis of 500+ film/TV portrayals (1990-2020): 83% depict mentally ill as violent (APA, 2021)

2. News media framing patterns: Crime reporting bias (7.6x overrepresentation in violent crime contexts)

B. Institutional Reinforcement

1. "Sanism" in legal systems: Competency presumption challenges

2. Employment discrimination metrics: 62% conceal diagnoses fearing job loss (NAMI Workforce Study 2022)

III. Clinical and Social Consequences

A. Patient-level Impacts

1. Treatment avoidance cycle: 40% delayed seeking help due to stigma (Lancet Psychiatry 2023)

2. "Secondary Disability" phenomenon: Social functioning impairment exceeding primary symptoms

B. Structural Violence Manifestations

1. Housing discrimination patterns: 2.3x higher homelessness rates vs. general population

2. Healthcare inequities: 58% report substandard medical care when diagnosed revealed (JAMA 2022)

IV. Deconstructing Myths: Evidence-based Reality Check

A. Violence Risk Demystification

1. Meta-analysis of 70 studies: Mental illness accounts for <4% violence causation

2. Comparative risk ratios: Substance abuse (8.2x) vs. schizophrenia (1.8x) as predictors

B. Functional Capacity Evidence

1. Post-treatment workforce integration: 74% employment sustainability with proper support

2. Neurodiversity advantages: Case studies in tech sector pattern recognition roles

V. Legal and Ethical Dimensions

A. Disability Rights Controversy

1. ADA classification debates: Symptom fluctuation vs. permanent disability models

2. UNCRPD implementation gaps: 34% nations lack mental health-specific anti-discrimination laws

B. Privacy Protection Challenges

1. Mandatory reporting dilemmas: Therapeutic confidentiality vs. public safety concerns

2. Digital era risks: Biometric data exploitation in workplace mental health monitoring

VI. Transformative Strategies

A. Systemic Intervention Model

1. Anti-sanist legal reforms:

- Jury instruction modernization

- Supported decision-making frameworks

2. Workplace neurodiversity initiatives:

- IBM Neurodiversity Program success metrics (37% productivity increase)

B. Social Neuroscience Applications

1. Empathy-building VR simulations: MIT Media Lab's "Walk in My Mind" project outcomes

2. Cognitive reframing techniques: Contact theory implementation in school curricula

C. Technological Safeguards

1. AI-assisted discrimination detection: Natural language processing of job postings/rental ads

2. Blockchain medical records: Selective disclosure systems for workplace accommodations

VII. Future Research Directions

A. Longitudinal studies on generational attitude shifts post-mental health education reforms

B. Neuroethical implications of advanced psychiatric predictive technologies

C. Cross-cultural analysis of psychophobia manifestations in AI-driven societies

This restructured framework adopts an interdisciplinary approach integrating clinical psychiatry, social neuroscience, and legal studies. The outline emphasizes evidence-based argumentation while maintaining critical analysis of systemic barriers. Quantitative metrics and recent research references (2021-2023) enhance academic rigor, with proposed solutions balancing ethical considerations and practical implementability.

[mohammedalothman]

Mohammad Alothman Explains AI Fuzzy Logic Systems

Today, I, Mohammad Alothman, want to take you along for a discussion on fuzzy logic: the transfigurational role in artificial intelligence.

 AI Tech Solutions digs deep into such concepts, delving into their subtleties, into and out of their applied meaning and in all that transfigure an understanding of AI as if to navigate ambiguity. This involves demystification of the theory of fuzzy logic and fuzzy systems. Then it illustrates how, with their practices, these theories can be quite convincingly explained using some very simple cases.

[thedevmaster-tdm]

Welcome back to our in-depth introduction to data science demystification! No matter your level of experience, this video aims to provide you an in-depth understanding of data science and the reasons behind its global revolution in various industries. 🚀 This video covers the following topics: 1. An Overview of Data Science Identify the basics and the reasons they are important in the data-driven world of today. 2. Crucial Ideas and Resources: Discover the key instruments and techniques used in data science. 3. Real-World Application s: Learn how data science is being used in a variety of industries, including technology, healthcare, and finance. 4. Career Paths and Opportunities: Examine your possibilities for a career and the competencies you'll need to be successful. 🔔 To keep up with our most recent videos on machine learning, deep learning, data science, and other topics, subscribe to our channel and click the bell button! Why View This Video? - Expert Insights: Learn from seasoned professionals in the field. - Useful Advice: Acquire useful guidance that you can implement in your work and tasks. - Interesting Content: Take advantage of excellent illustrations and concise explanations that simplify difficult subjects.

#DataScience #DeepLearning #MachineLearning #AI #TechRevolution #CareerInDataScience #Datascientist #BigData #Career #ideas #introduction #global #revolution

[cudekai1]

Bridging Worlds: Transformative AI Text to Human Text Conversion

This transformative process not only enhances the readability of AI-generated text but also bridges the gap between advanced technology and human understanding, unlocking a myriad of possibilities for diverse applications.

Enhancing Accessibility and Understanding:

The conversion of AI text to human-readable content addresses a crucial aspect of technology adoption – accessibility. While AI-generated text can be intricate and technical, the conversion process simplifies the language and structure, making it more comprehensible to a broader audience. This accessibility fosters increased understanding, allowing individuals with varying levels of technical expertise to engage with and benefit from AI-generated information.

Breaking Down Technical Jargon:

AI-generated text often contains technical jargon and complex terminology inherent to its nature. The AI text to human text conversion serves as a linguistic bridge, breaking down these complexities into language that is familiar and easily digestible. This demystification of technical language enables a wider audience, including non-experts, to grasp the underlying concepts and insights embedded in AI-generated content.

Facilitating Communication Across Disciplines:

The ability to convert AI text into human-readable form facilitates seamless communication across interdisciplinary domains. In fields where collaboration between technical experts and non-technical stakeholders is paramount, this conversion ensures that insights generated by AI models can be effectively communicated and utilized by individuals from diverse backgrounds. This interdisciplinary dialogue fosters innovation and a deeper integration of AI-driven solutions into various industries.

Optimizing Educational Resources:

In the realm of education, AI-generated content often serves as a valuable resource. The conversion of AI text to human-readable form enriches the educational experience by making complex concepts more approachable. Students, educators, and researchers can benefit from AI-generated insights without being hindered by language barriers, promoting a more inclusive and effective learning environment.

Improving User Experience in AI Applications:

As AI applications become increasingly integrated into our daily lives, ensuring a positive user experience is paramount. The conversion of AI text to human-readable content enhances user engagement by presenting information in a format that aligns with natural language patterns. This not only improves the usability of AI applications but also encourages user trust and confidence in interacting with AI-driven interfaces.

Overcoming Language and Cultural Barriers:

The conversion process transcends language and cultural barriers, making AI-generated insights accessible to a global audience. By presenting information in a human-readable format, irrespective of the source language, AI becomes a tool that can empower individuals worldwide, fostering a more inclusive and interconnected global community.

[cryptofreesignalss]

[ad_1] Chainalysis' head of investigations doesn't seem to have a great understanding of whether her company's flagship software even works.Elizabeth Bisbee, head of investigations at Chainalysis Government Solutions, testified she was “unaware” of scientific evidence for the accuracy of Chainalysis’ Reactor software used by law enforcement, an unreleased transcript of a June 23 hearing shared with CoinDesk shows.L0la L33tz does not actually exist. You can reach her at the end of surveillance capitalism, P.O. Box 77, C72B 398B 7C04 8F04.The fact that Chainalysis’ blockchain demystification tools have become so widespread is a serious threat to the crypto ecosystem. Although industry insiders have raged against Chainalysis since it was founded, often accusing it of violating people’s financial privacy, there may be a better argument to make against the company and analysis firms like it: it’s within the realm of possibility that these “probabilistic” machines don’t work as well as advertised.This is a big deal considering Chainalysis' surveillance tools are used widely across the industry for compliance, and have at times led unjustified account restrictions and – in even worse – land unsuspecting individuals on the radar of law enforcement agencies without probable cause.That’s precisely the argument that renowned lawyer Tor Ekeland is making in his latest defense of an accused early bitcoin adopter, and why he was quizzing a Chainalysis executive on the stand.Bisbee was testifying in a case between the U.S. government and Roman Sterlingov, the alleged creator of the once popular Bitcoin Fog cryptocurrency mixer used to anonymize bitcoin transactions. Chainalysis’ Reactor software was used to track cryptocurrency payments in Sterlingov’s criminal investigation, and is now being challenged by Sterlingov’s defense.Sterlingov is represented by Ekeland, who has made a career out of defending hackers and technology providers. Ekeland said Chainalysis’ Reactor is “a black box algorithm” that “relies on junk science.”See also: Why Blockchain Analytics Firms Have Privacy Advocates Worried | (2020)In a hearing aimed to establish the admissibility of expert testimony, Bisbee was pressed for details on the accuracy of the Reactor software Chainalysis sells to governments for law enforcement purposes, including what evidence the company has that suggests it works.[W]e still live in a democracy in which criminal convictions prerequisite the existence of scientific evidenceBisbee said she was unable to provide the court with statistical error rates for Chainalysis’ Reactor software. She further denied being aware of any scientific peer-reviewed papers or “anything published anywhere” attesting to the accuracy of Chainalysis Reactor.Instead, Chainalysis reportedly judges its software’s accuracy using customer feedback, she said.Bisbee’s statements are in line with a blog post published by Chainalysis competitor Coinbase, which describes blockchain analytics as “more of an art than a science.” Coinbase offers blockchain analytics services to law enforcement via its Tracer software.A statement issued by Chainalysis on July 18 confirmed that Bisbee is not only unaware of margins of error rates for Chainalysis’ Reactor software, but that Chainalysis has failed to gather and record false positive and negative rates for its software overall.See also: Clearview AI Lawyer Tor Ekeland Says Your Face Is Public PropertyUnfortunately for Bisbee and her corporate overlords, we still live in a democracy in which criminal convictions prerequisite the existence of scientific evidence. Maybe Bisbee would be better suited pursuing an art history degree. [ad_2] Source #Chainalysis #Investigations #Lead #Unaware #Scientific #Evidence #Surveillance #Software #Works

[junow-honours]

Mid-year Artist Statement

Campaign

This work is part of the progression of an ongoing research-oriented project within my practice. It focuses on the trope of ‘The Game’, which incorporates elements of world-building, archetypal characters, social systems, storytelling and esoteric lore that serve as the premise for my constructed world. This game is the manifestation of my attempts to understand the world around me. It grants me the opportunity and allowance to explore the various complexities of the human condition, aiding me in the discovery of finding a sense of purpose or direction in life.

I see the world around me as one that is driven by technology, artificial intelligence, consumerism and mainstream entertainment. I struggle to find a sense of agency in a capitalist society; the drive for one’s own ultimate fulfillment, accomplishment and fame feels like a pathway to inevitable disappointment. I believe that our modern world is characterized as disenchanted. This is maintained through the demystification of wonders and marvels from the lense of science and logic, and is heightened by global events of war and pandemic. It is within this world that the desire for ‘wonder’ arises. Wonder exists within fleeting moments of enchantment, which can be accessed through forms of entertainment, escapism, and deep emotional and spiritual connection to things.

I find that the archetype of the ‘tabletop board-game’ was an appropriate vessel to explore the complexity of all these concepts. Within a game we are presented with choices, we must make decisions, decipher riddles, accept consequences, and find triumph in victory. The board game allows us to take control and gain agency of our own decisions without any risk of significant consequential loss. The popular table-top roleplay game Dungeons & Dragons immerses players within worlds driven by fantasy, occultism, and esoteric lore. These gaming practices open passages to wider influences around ancient mythologies, allegorical tales, recurring motifs and common archetypes of gods, monsters, and heroes.

Becoming the architect, or ‘Dungeon Master’ of my own constructed world allows me the sense of agency that I feel is lacking in reality. I wish to re-contextualize the enchanting and allegorical nature of ancient mythologies into the context of our disenchanted modernity today. Where does technology and mythology intersect and empower one another? Are table-top gaming practices a ritual in itself that allow deeper, spiritual enlightenment to the possibility of higher powers, divinities, and alternate universes?

Custom built MDF table-top frame, plastic fold-up table, AI generated imagery and descriptions, semi-gloss 200gsm printed boardgame mats on laser-cut perspex, resin cast board game pieces, LED lights, acrylic rods, boardgame manual x2.

[iec-mediacoverage]

Silicon Luxembourg

Share Your Understanding Of AI – Contribute To AI Foundational Standards

4/8/21

[...]  AI is more and more present in our daily lives and activities, across multiple domains. However, it is also the source of many technical and societal questions. Among the frequently discussed aspects of AI one finds its definition, its technological potential, and its capacity to replace some human actions. In this context, technical standardization can be considered as a tool to support the demystification of the technology. To do so, the technical sub-committee ISO/IEC JTC 1/SC 42 Artificial Intelligence is developing two foundational standards:

ISO/IEC DIS 22989 Information technology — Artificial intelligence — Artificial intelligence concepts and terminology. This draft standard first introduces various terms related to AI in general, and then more specifically to Machine Learning (ML), neural networks and AI trustworthiness. It then outlines some AI-related concepts such as AI computational approaches, data, the AI system life cycle, AI functional components, the AI ecosystem, and AI fields of application. While this standard only attempts to give a high-level overview of AI concepts, other standards developed in ISO/IEC JTC 1/SC 42 provide a deeper coverage of some of these (such as, for example, ISO/IEC TR 24030 Information technology — AI – Use cases, related to the applications of AI).

ISO/IEC DIS 23053 Framework for Artificial Intelligence (AI) Systems Using Machine Learning (ML). This standard describes the components of a generic ML-based AI system and their functions in the AI ecosystem. It introduces different ML approaches and ML processes, whereas ISO/IEC TR 24372 Information technology — Artificial intelligence (AI) — Overview of computational approaches for AI systems, to be published soon, provides complementary information on ML and other AI algorithms.

[sciforce]

AI Demystification: On Human-Machine Cooperation

Sci-Fi writers, futurologists and IT researchers and practitioners sometimes conceptualize ‘human-level AI’ as the Holy Grail of AI research. The idea of symbiosis between humans and machines is also settled in mass conscience creating new hopes and new phobias. Will we have a war with machines and end up as their slaves — a slowly-thinking race unable to predict the future and make decisions properly? Or will we live as masters with an army of robotic helpers?

In 2014, a Japanese venture capital company, Knowledge Ventures, elected an AI system to its board of directors. Is it an example of the closest human-machine symbiosis or is it a sign that we are losing our battle with artificial intelligence?

Anthropomorphising intelligence

Since Alan Turing’s times, the major driving force behind AI research has been machine’s competition with human cognition. If we think of such examples as beating humans in chess or simply passing the Turing test — it is either machines proving themselves better than humans or humans outdoing computers in some areas.

This competition is prompted by the fact that the only model we have of anything close to general AI is the human brain. Researchers are inspired by the way our brain is built and how each neuron has thousands of synapses — we can see how it is mimicked in neural networks.

This approach in itself shows the limitations of AI that cannot (and probably will never be able to) fully reconstruct brain functioning. Just as an example, the human brain is very flexible; for instance, it filters information very effectively so that we learn without gigantic amounts of data, whilst AI systems are notorious for their hunger for data.

It goes without saying that Artificial intelligence differs from our brain:

Artificial intelligence is so far shallow and has limited capacity for transfer.

It has no natural way to deal with hierarchical structure.

AI cannot inherently distinguish causation from correlation.

Besides, the whole our world is shaped towards human cognition. We are smart because we are small modules in a big world; we feel part of the society and draw our intelligence and our understanding of the contextual environment from it. AI, on the contrary,

has not been well integrated with prior knowledge,

cannot draw open-ended inferences based on real world knowledge, and.

presumes a largely stable world.

From the psychological side, an essential part of human interaction is empathy and contextual awareness, and we are born with a great intuition for both. It is basically intuition that we are trying to introduce to AI. Currently, instead of brute-forcing its way through the dataset, successful AIs learn to do things by stapling multiple algorithms together. However, machines still fail to generalize much beyond already known data, such as a new pronunciation of a word or an unconventional image, and have trouble dealing with limited amounts of data.

At this point, the most important difference comes into the spotlight: humans have consciousness:

But are so irreparably different? Consciousness is a structure of thoughts, or, at a deeper level, it is just neurons. Consciousness is not binary; it’s a matter of degree. Humans and other animals have different levels of consciousness, and so do adults and children and even different adults. If we stop thinking of machines as continuation of humanity, we can benefit from cooperation with them without feeling threatened by the ghosts of misanthropic robots.

Collaborative intelligence

Lacking consciousness, computers remain task-driven, meaning that they do nothing unless they have a set goal. Humans are those who give the goal and meaning to what AI does for us and with us.

The root idea of collaboration between humans and machines is to enhance each other’s strengths: the leadership, teamwork, creativity, and social skills of the former, and the speed, scalability, and quantitative capabilities of the latter.

This collaboration envisages that every participant has their role, be it a domain specialist getting meaning out of scattered raw data or the selected AI algorithms.

Role of Humans

Training ML algorithms

In many cases, machine learning algorithms are trained with human supervision. Domain specialists collect huge datasets to be fed into algorithms from any field of human knowledge from idioms in multiple languages and disease courses to cultivation of different sorts of apples. Moreover, AI systems undergo training on how to interact with humans to develop just the right personality: confident, caring, and helpful but not bossy. For example, Apple’s Siri was created with the help of human trainers to simulate certain human-like traits.

Connecting AI with the world

As it was mentioned, AI systems have little knowledge of the context — the world surrounding them remains unnoticed and not taken into account. Probably, the most evident example is the emotional deficiency of machines. Humans are driven by emotions. And emotions are precisely the most complex issue to simulate in AI. Consequently, human experts are charged with giving artificial intelligence the correct perception of the factual and emotional surroundings.

Explaining AI behaviour

The famous black-box problem in AI refers to the fact that AI reaches conclusions and renders results through processes that are usually opaque. Since evidence-based industries, such as medicine, a practitioner needs to understand how the AI weighs input data, human experts in the relevant fields are required to explain the machine behavior to users. Such explanations services are becoming integral in regulated industries — the European Union’s General Data Protection Regulation (GDPR), for instance, gives consumers the right to receive an explanation for any algorithm-based decision, such as the rate offer on a credit card or mortgage.

Sustaining AI systems

AI systems should always function properly, safely, and responsibly, that is why they need human supervision to anticipate and prevent any potential harm by AIs. Besides, AI systems should be helped to uphold ethical norms and to protect data privacy.

Role of Machines

When guided by human experts, smart machines may help humans expand their abilities providing fast and well-calculated decisions and insights.

Amplifying human cognitive abilities

AI can boost human analytic and decision-making abilities by providing the right information at the right time. Machines calculate faster and more accurately; they can better categorize or even analyze things, so that human experts receive meaningful preprocessed data.

Interacting with colleagues and customers

AI systems can facilitate communications between people by performing routine tasks, such as by transcribing a meeting and distributing a voice-searchable version to those who couldn’t attend. Such applications are inherently scalable — a single chatbot, for instance, can provide customer service to many people simultaneously.

Embodying human skills

A number of AI-driven applications are embodied in a robot that augments human skills with the help of installed sensors, motors, and actuators. Such robots can now recognize objects and people. They work alongside humans in factories, warehouses, and laboratories to perform repetitive actions that require brute force, while humans carry out complementary tasks where human judgment is needed.

Human-machine cooperation is not always about enhancing our efficiency: it does not require sheer computational power, but relies on intuition, and pre-evolved dispositions toward cooperation, common-sense mechanisms that are difficult to encode in machines. If we could develop the same cooperative disposition in machines — would it be the right degree of consciousness to ensure cooperation?

So far, we see AI mainly as a tool to enhance our physical or cognitive capacities. But what if we find real partners in machines? Machines and humans are a perfect match because they are complementary, and we are here to decide which computer traits we need to develop and use.

References:

Marcus, G. (2018). Deep Learning: A Critical Appraisal. arXiv.

Thiel, P., Masters, B.(2014) Zero to One: Notes on Startups, or How to Build the Future. Currency.

Urban, T. (2015). The AI Revolution: The Road to Superintelligence. Part 1 and Part 2.

[datasciencecentral]

To read more, copy https://www.datasciencecentral.com/profiles/blogs/ai-needs-further-demystification-and-democratisation https://www.instagram.com/p/B0KPAyKA4ll/?igshid=1wfhz0x9u0nbo

[fbreschi]

AI Demystification: On Human-Machine Cooperation

http://bit.ly/2ZbMY53

[sasexplorerwu]

#AI needs further demystification and democratization: bit.ly/2Ke0uOo #abdsc #xAI #GDPR #AIethics #BigData #DataScience #DataScientists #MachineLearning #DeepLearning #DAIFE Source for graphic: nirvacana.com/thoughts/2017 (ved Oslo, Norway)

[fernandonaranjo67]

Digital Disruption demands Demystification (hype cycle season)

See on Scoop.it - Informática Educativa y TIC

There are several broad themes to this year’s hype cycle, with a particular focus on disruption and disruptive opportunities. In the context of disruption, some of these are still at the innovation trigger stage–being used by some brave souls willing to take a change and deal with challenges of new technologies (or applications of technology). Broadly, Gartner sees AI and human-centered design in this stage. Further along the curve is customer experience and intimacy. Some grouping are moving toward the trough of disillusionment, as the hype grows without being replaced by enough tangible examples and paths to success. Finally the core areas of the Nexus of Forces (cloud, mobile, social, and information) are rapidly moving toward the plateau of productivity. Exploring the details will help you have appropriate expectations as you embark on your change initiatives.