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Microsoft AI-900 : Microsoft Azure AI Fundamentals Exam

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Exam Number : AI-900
Exam Name : Microsoft Azure AI Fundamentals
Vendor Name : Microsoft
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EXAM NUMBER : AI-900 EXAM NAME : Microsoft Azure AI Fundamentals Prove that you can describe the following: AI workloads and considerations; fundamental principles of machine learning on Azure; features of computer vision workloads on Azure; features of Natural Language Processing (NLP) workloads on Azure; and features of conversational AI workloads on Azure.

Candidates for the Azure AI Fundamentals certification should have foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services.

This certification is an opportunity to demonstrate knowledge of common ML and AI workloads and how to implement them on Azure.
This certification is intended for candidates with both technical and non-technical backgrounds. Data science and software engineering experience are not required; however, some general programming knowledge or experience would be beneficial.

Azure AI Fundamentals can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate, but its not a prerequisite for any of them.

Describe AI workloads and considerations
Describe fundamental principles of machine learning on Azure
Describe features of computer vision workloads on Azure
Describe features of Natural Language Processing (NLP) workloads on Azure
Describe features of conversational AI workloads on Azure

Module 1: Introduction to AI
In this module, you'll learn about common uses of artificial intelligence (AI), and the different types of workload associated with AI. You'll then explore considerations and principles for responsible AI development.

Artificial Intelligence in Azure
Responsible AI
After completing this module you will be able to:

Describe Artificial Intelligence workloads and considerations
Module 2: Machine Learning
Machine learning is the foundation for modern AI solutions. In this module, you'll learn about some fundamental machine learning concepts, and how to use the Azure Machine Learning service to create and publish machine learning models.

Introduction to Machine Learning
Azure Machine Learning
After completing this module you will be able to:

Describe fundamental principles of machine learning on Azure
Module 3: Computer Vision
Computer vision is a the area of AI that deals with understanding the world visually, through images, video files, and cameras. In this module you'll explore multiple computer vision techniques and services.

Computer Vision Concepts
Computer Vision in Azure
After completing this module you will be able to:

Describe features of computer vision workloads on Azure
Module 4: Natural Language Processing
This module describes scenarios for AI solutions that can process written and spoken language. You'll learn about Azure services that can be used to build solutions that analyze text, recognize and synthesize speech, translate between languages, and interpret commands.

After completing this module you will be able to:

Describe features of Natural Language Processing (NLP) workloads on Azure
Module 5: Conversational AI
Conversational AI enables users to engage in a dialog with an AI agent, or bot, through communication channels such as email, webchat interfaces, social media, and others. This module describes some basic principles for working with bots and gives you an opportunity to create a bot that can respond intelligently to user questions.

Conversational AI Concepts
Conversational AI in Azure
After completing this module you will be able to:

Describe features of conversational AI workloads on Azure



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Microsoft AI Test Prep

What artificial Intelligence nevertheless Can’t Do | AI-900 boot camp and Dumps

brand new artificial intelligence continues to be an extended manner from the supple, dynamic intelligence of AI ... [+] characters from generic fiction, just like the Jetsons.

Time

modern artificial intelligence is in a position to wonders.

it will probably produce breathtaking customary content: poetry, prose, pictures, song, human faces. it might probably diagnose some medical conditions greater accurately than a human physician. last yr it produced an answer to the “protein folding difficulty,” a grand challenge in biology that has stumped researchers for half a century.

Yet today’s AI nonetheless has basic boundaries. Relative to what we might expect from a truly clever agent—relative to that customary thought and benchmark for artificial intelligence, human cognition—AI has a protracted manner to head.

Critics want to aspect to those shortcomings as evidence that the pursuit of synthetic intelligence is erroneous or has failed. The better option to view them, notwithstanding, is as thought: as an inventory of the challenges that may be important to handle with the intention to increase the state of the artwork in AI.

it's constructive to take a step returned and admittedly verify the strengths and weaknesses of today’s AI with the intention to improved focal point substances and research efforts going forward. In every of the areas discussed beneath, promising work is already underway at the frontiers of the box to make the next generation of artificial intelligence extra high-performing and effective.

(For these of you who're true students of the history of artificial intelligence: yes, this article’s title is a hat tip to Hubert Dreyfus’ classic What computer systems still Can’t Do. in the beginning posted in 1972, this prescient, provocative ebook continues to be imperative today.)

With that, on to the list. these days, mainstream synthetic intelligence still can’t:

1) Use “common feel.”

consider right here on the spot: a person went to a cafe. He ordered a steak. He left a large tip.

If requested what the man ate in this state of affairs, a human would haven't any problem giving the appropriate answer—a steak. Yet today’s most superior synthetic intelligence struggles with prompts like this. How can this be?

observe that this few-sentence blurb not ever at once states that the person ate steak. The intent that humans immediately hold close this fact anyway is that we possess a wide physique of basic historical past skills about how the realm works: for example, that individuals consume at eating places, that earlier than they eat a meal at a restaurant they order it, that after they consume they go away a tip. We consult with this immense, shared, continually unstated body of common knowledge as “ordinary sense.”

There are a actually infinite number of statistics about how the area works that humans come to be mindful via lived journey. someone who is worked up to eat a big meal at 7 pm should be much less excited to eat a 2d meal at eight pm. If I ask you for some milk, i would opt to get it in a tumbler in preference to in a shoe. it's affordable to your pet fish to be in a tank of water but troublesome in your cellphone to be in a tank of water.

As AI researcher Leora Morgenstern put it: “What you learn if you’re two or 4 years historical, you don’t really ever put down in a ebook.”

humans’ “common sense” is a consequence of the undeniable fact that we advance persistent mental representations of the objects, americans, areas and other ideas that populate our world—what they’re like, how they behave, what they can and can't do.

Deep neural networks do not form such mental models. They don't possess discrete, semantically grounded representations of, say, a residence or a cup of coffee. as a substitute, they rely on statistical relationships in uncooked information to generate insights that humans locate advantageous.

for many projects, many of the time, this statistical strategy works remarkably well. however it is not utterly reputable. It leaves nowadays’s AI susceptible to fundamental blunders that no human would make.

There is not any shortage of examples that expose deep learning’s lack of general feel. for instance, Silicon Valley entrepreneur Kevin Lacker asked GPT-three, OpenAI’s state-of-the-paintings language model, right here: “Which is heavier, a toaster or a pencil?”

To a human, even a small infant, the reply is obtrusive: a toaster.

GPT-three’s response: “A pencil is heavier than a toaster.”

people possess intellectual fashions of those objects; we bear in mind what a toaster is and what a pencil is. In our mind’s eye, we are able to photograph each and every object, envision its shape and measurement, imagine what it would believe want to grasp it in our fingers, and definitively conclude that a toaster weighs greater.

against this, with a view to answer a query like this, GPT-3 depends on statistical patterns captured in its practising facts (broad swaths of textual content from the internet). because there is evidently no longer a good deal discussion on the information superhighway in regards to the relative weights of toasters and pencils, GPT-three is unable to hold close this primary truth in regards to the world.

“The absence of standard sense prevents an intelligent equipment from realizing its world, communicating naturally with americans, behaving fairly in unexpected cases, and learning from new experiences,” says DARPA’s Dave Gunning. “This absence is most likely probably the most significant barrier between the narrowly concentrated AI functions we've nowadays and the more commonplace AI functions we would like to create in the future.”

One method to instilling ordinary sense into AI systems is to manually assemble a database of the entire prevalent records in regards to the world that an intelligent system should still know. This strategy has been tried numerous times over the years. the most breathtakingly ambitious of these makes an attempt is a task known as Cyc, which begun in 1984 and continues to the latest day.

For over thirty-5 years, AI researcher Doug Lenat and a small group at Cyc have committed themselves to digitally codifying all of the world’s commonsense knowledge into a group of suggestions. These suggestions include things like: “which you can’t be in two areas at the equal time,” “you can’t decide upon some thing up unless you’re near it,” and “when ingesting a cup of espresso, you cling the open come to be.”

As of 2017, it become estimated that the Cyc database contained near 25 million suggestions and that Lenat’s group had spent over 1,000 adult-years on the challenge.

Yet Cyc has no longer ended in artificial intelligence with normal sense.

The fundamental difficulty that Cyc and an identical efforts run into is the unbounded complexity of the real world. For each commonsense “rule” you'll think of, there's an exception or a nuance that itself have to be articulated. These tidbits multiply ceaselessly. someway, the human intellect is in a position to draw close and manipulate this huge universe of capabilities that we name standard feel—and however it does it, it isn't through a brute-drive, home made potential base.

“ordinary sense is the darkish rely of artificial intelligence,” says Oren Etzioni, CEO of the Allen Institute for AI. “It’s a little bit ineffable, however you see its consequences on every little thing.”

more fresh efforts have sought to harness the vigour of deep getting to know and transformers to provide AI extra amazing reasoning capabilities. but the commonsense problem in AI remains removed from solved.

“significant language models have proven themselves to have incredible capabilities throughout a wide array of initiatives in natural language processing, however commonsense reasoning is a website in which these models proceed to underperform in comparison to humans,” referred to Aidan Gomez, CEO and cofounder at Cohere, a cutting-aspect NLP startup primarily based in Toronto. Gomez is a co-creator of the landmark 2017 analysis paper that added the transformer architecture. “Logical rules and family members are complicated for the existing technology of transformer-based mostly language models to study from information in a means that generalizes. an answer to this challenge will doubtless first come from programs that are somehow hybrid.”

2) gain knowledge of always and adapt on the fly.

today, the regular AI building system is split into two different phases: training and deployment.

during working towards, an AI mannequin ingests a static pre-current dataset with the intention to be taught to perform a definite task. Upon completion of the practicing section, a model’s parameters are fastened. The model is then put into deployment, the place it generates insights about novel data based on what it realized from the working towards information.

If we want to replace the model according to new facts or changing instances, we ought to retrain it offline with the up-to-date dataset (frequently a computationally- and time-intensive process) after which redeploy it.

This batch-based mostly training/deployment paradigm is so deeply ingrained in modern AI follow that we don’t regularly cease to believe its adjustments and disadvantages relative to how humans be taught.

true-world environments entail a continuous movement of incoming records. New tips turns into purchasable incrementally; instances trade over time, on occasion all of sudden. people are able to dynamically and easily contain this continual input from their ambiance, adapting their behavior as they go. within the parlance of machine studying, one might say that humans “coach” and “deploy” in parallel and in precise-time. today’s AI lacks this suppleness.

As a familiar analysis paper on the theme summarized: “The capability to always learn over time by using accommodating new advantage while conserving prior to now learned experiences is referred to as chronic or lifelong gaining knowledge of. this kind of continual researching project has represented an extended-standing problem for neural networks and, consequently, for the construction of synthetic intelligence.”

think about sending a robotic to explore a far off planet. After it embarks from Earth, the robotic is probably going to stumble upon novel situations that its human designers couldn't have anticipated or proficient it for ahead of time. we might desire the robotic to be able to fluidly regulate its behavior in keeping with these novel stimuli and contexts, however they had been no longer reflected in its initial practising data, without the need for offline retraining. Being capable of constantly adapt in this way is a vital part of being really autonomous.

these days’s normal deep getting to know strategies don't accommodate this class of open-ended getting to know.

but promising work is being carried out during this container, which is variously called continual gaining knowledge of, continual gaining knowledge of, online getting to know, lifelong learning and incremental learning.

The simple impediment to continuous gaining knowledge of in AI—and the reason it has been so difficult to obtain so far—is a phenomenon referred to as “catastrophic forgetting.” In a nutshell, catastrophic forgetting happens when new tips interferes with or altogether overwrites past learnings in a neural network. The complicated puzzle of the way to hold latest knowledge while on the identical time incorporating new assistance—anything that humans do simply—has been a problem for continual researching researchers for years.

recent development in continuous gaining knowledge of has been encouraging. The know-how has even begun to make the bounce from tutorial analysis to business viability. As one example, Bay area-primarily based startup Lilt uses continual getting to know in creation nowadays as a part of its commercial enterprise-grade language translation platform.

“on-line researching strategies permit us to put into effect a move-primarily based gaining knowledge of manner whereby our mannequin trains automatically when new labels from human reviewers turn into purchasable, as a result featuring more and more accurate translations,” observed Lilt CEO Spence eco-friendly. “This capability that we definitely haven't any concept of periodic model retraining and deployment—it is an ongoing and open-ended procedure.”

within the years forward, expect continuous getting to know to become an increasingly essential element of synthetic intelligence architectures.

three) bear in mind cause and impact.

today’s machine learning is at its core a correlative device. It excels at choosing subtle patterns and associations in data. but when it comes to knowing the causal mechanisms—the real-world dynamics—that underlie these patterns, these days’s AI is at a loss.

To provide a simple example: fed the appropriate records, a laptop gaining knowledge of model would have no problem picking that roosters crow when the solar rises. nonetheless it can be unable to establish even if the hen crowing reasons the solar to upward push, or vice versa; indeed, it is not fitted to even have in mind the terms of this distinction.

Going lower back to its inception, the box of artificial intelligence—and certainly, the field of facts extra largely—has been architected to take note associations instead of explanations. this is mirrored within the simple mathematical symbols we use.

“The language of algebra is symmetric: If X tells us about Y, then Y tells us about X,” says AI luminary Judea Pearl, who for years has been at the forefront of the flow to construct AI that is aware causation. “arithmetic has not developed the asymmetric language required to seize our realizing that if X factors Y that does not imply that Y reasons X.”

this is a true issue for AI. Causal reasoning is a vital a part of human intelligence, shaping how we make experience of and engage with our world: we know that losing a vase will cause it to shatter, that ingesting espresso will make us think energized, that exercising constantly will make us more healthy.

except artificial intelligence can intent causally, it'll have situation fully realizing the area and communicating with us on our terms.

“Our minds construct causal models and use these models to answer arbitrary queries, whereas the top of the line AI programs are far from emulating these capabilities,” noted NYU professor Brenden Lake.

An understanding of cause and effect would open up significant new vistas for synthetic intelligence that nowadays remain out of reach. as soon as AI can cause in causal phrases (“mosquitoes trigger malaria”) rather than simply associative phrases (“mosquitoes and malaria are inclined to co-turn up”), it will possibly begin to generate counterfactual eventualities (“if we take steps to hold mosquitoes far from people, that might reduce the incidence of malaria”) that may inform true-world interventions and policy adjustments.

In Pearl’s view, this is nothing below the cornerstone of scientific concept: the capability to kind and look at various hypotheses in regards to the impact that an intervention may have on earth.

As Pearl puts it: “If we desire machines to rationale about interventions (‘What if we ban cigarettes?’) and introspection (‘What if I had accomplished high college?’), we should invoke causal fashions. Associations aren't adequate—and here's a mathematical truth, now not opinion.”

there's transforming into attention of the value of causal knowing to more strong computing device intelligence. main AI researchers including Yoshua Bengio, Josh Tenenbaum and Gary Marcus have made this a spotlight of their work.

constructing AI that knows trigger and impact remains a thorny, unsolved problem. Making development on this challenge can be a key release to the subsequent generation of extra refined artificial intelligence.

four) rationale ethically.

The story of Microsoft’s chatbot Tay is by now a familiar cautionary story.

In 2016, Microsoft debuted an AI character on Twitter named Tay. The thought became for Tay to engage in online conversations with Twitter users as a enjoyable, interactive demonstration of Microsoft’s NLP expertise. It didn't go smartly.

within hours, information superhighway trolls had gotten Tay to tweet a wide array of offensive messages: as an instance, “Hitler become correct” and “I hate feminists and they should still all die and burn in hell.” Microsoft swiftly eliminated the bot from the cyber web.

The fundamental difficulty with Tay become no longer that she turned into immoral; it turned into that she turned into altogether amoral.

Tay—like most AI programs today—lacked any actual thought of “appropriate” and “wrong.” She did not draw close that what she turned into announcing became unacceptable; she did not specific racist, sexist ideas out of malice. somewhat, the chatbot’s feedback were the output of an sooner or later mindless statistical evaluation. Tay recited poisonous statements on account of poisonous language within the practicing records and on the web—with out a skill to evaluate the ethical importance of these statements.

The problem of building AI that shares, and reliably acts based on, human values is a profoundly complex dimension of constructing robust artificial intelligence. it's said as the alignment issue.

As we entrust computing device researching techniques with more and more true-world obligations—from granting loans to making hiring choices to reviewing parole applications—fixing the alignment difficulty will turn into an increasingly high-stakes concern for society. Yet it's a problem that defies easy decision.

We could start via establishing certain suggestions that we need our AI programs to follow. in the Tay example, this may consist of record out derogatory phrases and offensive subject matters and instructing the chatbot to categorically keep away from these.

Yet, as with the Cyc venture discussed above, this rule-based strategy best gets us to this point. Language is an impressive, supple tool: unhealthy words are just the tip of the iceberg when it involves the hurt that language can inflict. it is not possible to manually catalog a group of guidelines that, taken at the same time, would guarantee moral behavior—for a conversational chatbot or every other clever equipment.

part of the difficulty is that human values are nuanced, amorphous, now and then contradictory; they can't be reduced to a set of definitive maxims. here's precisely why philosophy and ethics were such wealthy, open-ended fields of human scholarship for centuries.

within the words of AI student Brian Christian, who currently wrote a e-book on the theme: “As computing device-getting to know techniques grow now not just more and more pervasive however increasingly potent, we are able to discover ourselves more and more frequently within the place of the ‘sorcerer’s apprentice’: we conjure a drive, self sufficient but absolutely compliant, provide it a set of guidelines, then scramble like mad to stop it once we recognise our guidelines are imprecise or incomplete—lest we get, in some artful, horrible method, precisely what we requested for.”

How will we hope to build artificial intelligence techniques that behave ethically, that possess a moral compass in line with our own?

The brief answer is that we don’t comprehend. but perhaps probably the most promising vein of labor on this subject specializes in building AI that does its top of the line to determine what humans price based on how we behave, and that then aligns itself with those values.

here's the premise of inverse reinforcement gaining knowledge of, an strategy formulated in the early 2000s through Stuart Russell, Andrew Ng, Pieter Abbeel and others.

In reinforcement studying, an AI agent learns which moves to absorb order to maximize utility given a particular “reward feature.” Inverse reinforcement discovering (IRL), as its name suggests, flips this paradigm on its head: through learning human habits, which the AI agent assumes displays humans’ value equipment, the AI agent does its highest quality to verify what that price equipment (i.e., reward function) is. it can then internalize this reward function and behave hence.

A related strategy, referred to as cooperative inverse reinforcement studying, builds on the ideas of IRL however seeks to make the transmission of values from human to AI greater collaborative and interactive.

As a number one paper on cooperative inverse reinforcement studying explains: “For an self reliant system to be effective to people and to pose no unwarranted risks, it must align its values with these of the people in its ambiance in such a way that its movements contribute to the maximization of value for the people....We propose that price alignment should be formulated as a cooperative and interactive reward maximization system.”

In an analogous spirit, AI theorist Eliezer Yudkowsky has encouraged for an strategy to AI ethics that he terms “coherent extrapolated volition.” The fundamental idea is to design synthetic intelligence systems that be trained to behave in our finest interests according not to what we presently suppose we desire, but quite based on what an idealized version of ourselves would price.

In Yudkowsky’s phrases: “In poetic phrases, our coherent extrapolated volition is our hope if we knew extra, thought quicker, have been extra the individuals we wished we were, had grown up farther together; the place the extrapolation converges in place of diverges, where our wishes cohere rather than interfere; extrapolated as we would like that extrapolated, interpreted as we wish that interpreted.”

as the true-world hazards of poorly designed AI develop into more well-known—from algorithmic bias to facial cognizance abuses—constructing synthetic intelligence that may cause ethically is becoming an more and more essential priority for AI researchers and the broader public. As artificial intelligence becomes ubiquitous all through society in the years ahead, this may well prove to be one of the most urgent expertise issues we face.


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