Eight years ago, I was packing my home and entire life in Mexico to move to the US to pursue a PhD in Ecology and Evolutionary Biology at the University of California-Irvine. Those were easier times, although it did not seem like it at the time. I spent a few months worth of income to pay for paperwork to apply for an F-1 student visa, and to pay for other documents to enroll as a graduate student. This was after I dedicated months to emailing professors everywhere in the US, hoping that one of them would reply to my email and would invite me to apply to join their lab. It was also after spending time and money paying for standardized tests, official document translations, and application fees. It was a one-and-a-half-year process but in July 2012, I was finally moving to the USA to pursue my PhD. It was a dream come true.

It was also a dream come true for the University of California because I had a full scholarship from my home country that paid for the entirety of my international tuition and fees, which were around $35,000 per year. My scholarship allowed me to pursue my PhD in the USA, and to UC Irvine it provided basically free labor as well as prestige.

I paid taxes and did all of the typical graduate student responsibilities. I also dedicated a lot of my time to doing outreach to bring science to underserved communities around Orange County and Southern California. By the time I graduated in 2017, I was a stellar student, with three publications with UC Irvine's name on them. I co-organized summer science camps for middle school girls that brought money and a good reputation to my university and program. I mentored students of all ages. I was a good citizen of my program, of my university, and of Orange County.

Like me, most international students leave their families and everything that they are comfortable with to pursue the dream of graduate school. They bring with them the hope of being welcomed and treated fairly by their American peers. I have experienced this, but I am one of the lucky ones.

It is no secret that international students and postdocs will withstand abuse and other injustices just so they can keep their visa, which is always tied to their university. Many universities receive international students without having a system to deal with the unique challenges that international students face, such as having no credit history, which complicates finding a place to live and leaves international students vulnerable to landlord abuse. Many international students are people of color, and universities, especially predominantly white institutions, do not have resources to ensure safety of these students within the university and in the community at large.

These challenges are further complicated due to a lack of community and support. Making friends in the US, especially if you are coming from Global South countries and/or non-Westernized countries, is extremely challenging. Many times, I have seen how western Europeans, Australians, and Canadians are rapidly accepted in the local community, while many Latinx, Asians, and Middle-Easterners are not.

There are over one million international students in the US. The ICE Student Ban may no longer be a threat, but universities still need to change how they handle international students. We are people too, but many universities have historically valued us only by the amount of money we bring. We improve higher education not only by the money that we bring, but by our unique perspectives, our research productivity, and our willingness to give back to American society.

Read more here:
An artificial intelligence algorithm designed to beat a video game takes on ecology and evolution - Massive Science

For general inquiries about the AAAI-20 program, please write to aaai20@aaai.org.

A selection of talks from AAAI-20 will be livestreamed and available on this page.

General Chair:

Francesca Rossi (IBM Research, USA)

Program Cochairs:

Vincent Conitzer (Duke University, USA)

Fei Sha (Google Research and University of Southern California, USA)

SPECIAL EVENT

The Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI-20) will be held February 7-12, 2020 at the Hilton New York Midtown, New York, New York, USA. The program chairs will be Vincent Conitzer (Duke University, USA) and Fei Sha (University of Southern California, USA). The purpose of the AAAI conference is to promote research in artificial intelligence (AI) and scientific exchange among AI researchers, practitioners, scientists, and engineers in affiliated disciplines. AAAI-20 will have a diverse technical track, student abstracts, poster sessions, invited speakers, tutorials, workshops, and exhibit and competition programs, all selected according to the highest reviewing standards. AAAI-20 welcomes submissions on mainstream AI topics as well as novel crosscutting work in related areas.

Additional details about AAAI-20 will be posted here as they become available.

Past AAAI Conferences

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AAAI 2020 Conference | Thirty-Fourth AAAI Conference on ...

Nowadays many misconceptions are there related to the words machine learning, deep learning and artificial intelligence(AI), most of the people think all these things are same whenever they hear the word AI, they directly relate that word to machine learning or vice versa, well yes, these things are related to each other but not the same. Lets see how.

Machine Learning:Before talking about machine learning lets talk about another concept that is called data mining. Data mining is a technique of examining a large pre-existing database and extracting new information from that database, its easy to understand, right, machine learning does the same, in fact, machine learning is a type of data mining technique.

Heres is a basic definition of machine learning Machine Learning is a technique of parsing data, learn from that data and then apply what they have learned to make an informed decision

Now a days many of big companies use machine learning to give there users a better experience, some of the examples are, Amazon using machine learning to give better product choice recommendations to there costumers based on their preferences, Netflix uses machine learning to give better suggestions to their users of the Tv series or movie or shows that they would like to watch.Deep Learning: Deep learning is actually a subset of machine learning. It technically is machine learning and functions in the same way but it has different capabilities.

The main difference between deep and machine learning is, machine learning models become better progressively but the model still needs some guidance. If a machine learning model returns an inaccurate prediction then the programmer needs to fix that problem explicitly but in the case of deep learning, the model does it by himself. Automatic car driving system is a good example of deep learning.

Lets take an example to understand both machine learning and deep learning Suppose we have a flashlight and we teach a machine learning model that whenever someone says dark the flashlight should be on, now the machine learning model will analyse different phrases said by people and it will search for the word dark and as the word comes the flashlight will be on but what if someone said I am not able to see anything the light is very dim, here the user wants the flashlight to be on but the sentence does not the consist the word dark so the flashlight will not be on. Thats where deep learning is different from machine learning. If it were a deep learning model it would on the flashlight, a deep learning model is able to learn from its own method of computing.Artificial intelligence: Now if we talk about AI, it is completely a different thing from Machine learning and deep learning, actually deep learning and machine learning both are the subsets of AI. There is no fixed definition for AI, you will find a different definition everywhere, but here is a definition that will give you idea of what exactly AI is.AI is a ability of computer program to function like a human brain

AI means to actually replicate a human brain, the way a human brain thinks, works and functions. The truth is we are not able to establish a proper AI till now but we are very close to establish it, one of the examples of AI is Sophia, the most advanced AI model present today. The reason we are not able to establish proper AI till now is, we dont know the many aspects of the human brain till now like why do we dream ? etc.

Why people relate machine learning and deep learning with artificial intelligence?Machine learning and deep learning is a way of achieving AI, which means by the use of machine learning and deep learning we may able to achieve AI in future but it is not AI.

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Tech writer Ben Dickson poses the question:

Should you feel bad about pulling the plug on a robot or switch off an artificial intelligence algorithm? Not for the moment. But how about when our computers become as smartor smarterthan us?

Philosopher Borna Jalenjak (above right) of the Luxembourg School of Business has been thinking about that. He has a chapter, The Artificial Intelligence Singularity: What It Is and What It Is Not, in Guide to Deep Learning Basics: Logical, Historical and Philosophical Perspectives, in which he explores the case for thinking machines being alive, even if they are machines. The book as a whole presents unique perspectives on ideas in deep learning and artificial intelligence, and their historical and philosophical roots.

Dickson explains,

Singularity is a term that comes up often in discussions about general AI. And as is wont with everything that has to do with AGI, theres a lot of confusion and disagreement on what the singularity is. But a key thing that most scientists and philosophers agree that it is a turning point where our AI systems become smarter than ourselves. Another important aspect of the singularity is time and speed: AI systems will reach a point where they can self-improve in a recurring and accelerating fashion.

Said in a more succinct way, once there is an AI which is at the level of human beings and that AI can create a slightly more intelligent AI, and then that one can create an even more intelligent AI, and then the next one creates even more intelligent one and it continues like that until there is an AI which is remarkably more advanced than what humans can achieve, Jalsenjak writes.

No. Wait. Is there clear evidence that less intelligent entities can simply create more intelligent ones? Consider,

A recent paper on the evolution of learning explores how computers could begin to evolve learning in the same way as natural organisms did. The authors use Avida, a software program for simulating evolution, to support their claim.

Avida was originally intended to demonstrate how Darwinian evolution, which could occur without design in nature, is supposed to work. However, as many have shown, the program actually ended up demonstrating quite conclusively the need for design. This latest paper on using Avida to simulate the evolution of learning has shown the same thing.

Many people do sincerely believe that higher intelligence can just somehow evolve from lower intelligence. But sincere belief isnt evidence. And Dickson stresses, To be clear, the artificial intelligence technology we have today, known as narrow AI, is nowhere near achieving such feat. So we are talking about whether superintelligent AI, if it ever arrives, can be considered alive.

And that is a more complex question than we might at first suppose. First there are 123 definitions of life out there, with different sciences tending to prefer their own:

It is surprisingly difficult to pin down the difference between living and non-living things

To make matters worse, different kinds of scientist have different ideas about what is truly necessary to define something as alive. While a chemist might say life boils down to certain molecules, a physicist might want to discuss thermodynamics.

The classic borderline case is viruses. They are not cells, they have no metabolism, and they are inert as long as they do not encounter a cell, so many people (including many scientists) conclude that viruses are not living, says Patrick Forterre, a microbiologist at the Pasteur Institute in Paris, France.

For his part, Forterre thinks viruses are alive, but he acknowledges that the decision really depends on where you decide to place the cut-off point.

Arguing that for the panpsychist view that electrons may be conscious, Tam Hunt makes the point that

Many biologists and philosophers have recognized that there is no hard line between animate and inanimate. J.B.S. Haldane, the eminent British biologist, supported the view that there is no clear demarcation line between what is alive and what is not: We do not find obvious evidence of life or mind in so-called inert matter; but if the scientific point of view is correct, we shall ultimately find them, at least in rudimentary form, all through the universe.

Niels Bohr, the Danish physicist who was seminal in developing quantum theory, stated that the very definitions of life and mechanics are ultimately a matter of convenience. [T]he question of a limitation of physics in biology would lose any meaning if, instead of distinguishing between living organisms and inanimate bodies, we extended the idea of life to all natural phenomena.

So there isnt a simple rule we can apply.

That said, some of the arguments for AI as a form of life sound suspiciously like the arguments around extraterrestrial beings:

Theres great tendency in the AI community to view machines as humans, especially as they develop capabilities that show signs of intelligence. While that is clearly an overestimation of todays technology, Jasenjak also reminds us that artificial general intelligence does not necessarily have to be a replication of the human mind.

That there is no reason to think that advanced AI will have the same structure as human intelligence if it even ever happens, but since it is in human nature to present states of the world in a way that is closest to us, a certain degree of anthropomorphizing is hard to avoid, he writes in his essays footnote.

Very well, but thats what they tell us about the so-far undetected extraterrestrials: They might be a form of life we dont recognize as such. One can never disprove such a proposition but, as before, it does not amount to evidence for anything.

Then there is the question of purpose:

There are different levels to life, and as the trend shows, AI is slowly making its way toward becoming alive. According to philosophical anthropology, the first signs of life take shape when organisms develop toward a purpose, which is present in todays goal-oriented AI. The fact that the AI is not aware of its goal and mindlessly crunches numbers toward reaching it seems to be irrelevant, Jalsenjak says, because we consider plants and trees as being alive even though they too do not have that sense of awareness.

Again, wait. Sophisticated computers have exclusively the purposes that humans program into them in our own interests, as do smart ovens and self-driving cars. These objects have no intrinsic purpose.

Plants have their own intrinsic purposes, which humans did not create, of growing and producing seeds. Humans can use plants and even trick them into doing something that is not part of their intrinsic purpose (seedless grapes, for example). But the original purpose is theirs. So we can give plants, but not computers, credit for purpose in life.

Jalenjak goes on to argue that AI can be alive even though it does not need to reproduce itself because it can, after all, just replace worn-out parts. But that fact alone is evidence that an AI entity is not alive. Life forms must reproduce themselves in a vast variety of ways because they are, generally, unitary beings, not a collection of swappable parts.

And what about self-improvement, which is regarded by some as part of a definition for life?

Todays machine learning algorithms are, to a degree, capable of adapting their behavior to their environment. They tune their many parameters to the data collected from the real-world, and as the world changes, they can be retrained on new information. For instance, the coronavirus pandemic disrupted may AI systems that had been trained on our normal behavior. Among them are facial recognition algorithms that can no longer detect faces because people are wearing masks. These algorithms can now retune their parameters by training on images of mask-wearing faces. Clearly, this level of adaptation is very small when compared to the broad capabilities of humans and higher-level animals, but it would be comparable to, say, trees that adapt by growing deeper roots when they cant find water at the surface of the ground.

Tree roots? Digging deeper for water is hardly their greatest accomplishment. They are very complex systems, used by the trees for, among other things, exchanging information with other trees:

Researchers are unearthing evidence that, far from being unresponsive and uncommunicative organisms, plants engage in regular conversation. In addition to warning neighbors of herbivore attacks, they alert each other to threatening pathogens and impending droughts, and even recognize kin, continually adapting to the information they receive from plants growing around them. Moreover, plants can talk in several different ways: via airborne chemicals, soluble compounds exchanged by roots and networks of threadlike fungi, and perhaps even ultrasonic sounds. Plants, it seems, have a social life that scientists are just beginning to understand.

Plants are not thought by botanists to be conscious but they do communicate extensively without a mind or brain. Nor, and this is the main point, do they need humans to program them or teach them anything. It all happens with or without our knowledge, let alone our involvement.

Jalenjak seems undeterred. He challenges us, Are characteristics described here regarding live beings enough for something to be considered alive or are they just necessary but not sufficient?

And Dickson responds,

Having just read I Am a Strange Loop by philosopher and scientist Douglas Hofstadter, I can definitely say no. Identity, self-awareness, and consciousness are other concepts that discriminate living beings from one another. For instance, is a mindless paperclip-builder robot that is constantly improving its algorithms to turn the entire universe into paperclips alive and deserving of its own rights?

So Dickson doesnt seem convinced. Still, he offers,

But like many other scientists, Jalsenjak reminds us that the time to discuss these topics is today, not when its too late. These topics cannot be ignored because all that we know at the moment about the future seems to point out that human society faces unprecedented change, he writes.

Maybe. But then again, maybe not.

The time to discuss this is now! implies that the scenario described must happen so we have no choice but to prepare. Perhaps the discussion we should have first is, how plausible are the arguments that whatever AI apocalypse is proposed must happen? In this case, Jalenjak didnt succeed in convincing Dickson that super AI should be considered alive. Maybe we dont need to have the discussion now, except as Sci-Fi Saturday food for thought.

The whole field could probably benefit from a dose of common sense and skepticism.

You may also enjoy:

Which is smarter? Babies or AI? Not a trick question Humans learn to generalize from the known to the unknown without prior programming and do not get stuck very often in endless feedback loops.

AI expert: Artificial intelligences are NOT electronic people. AI makes mistakes no human makes, so some experts are trying to adapt human cognitive psychology to machines. David Watson of the Alan Turing Institute fills us in on some of the limitations of AI and proposes fixes based on human thinking.

and

AI will fail, like everything else, eventually The more powerful the AI, the more serious the consequences of failure Overall, we predict that AI failures and premeditated malevolent AI incidents will increase in frequency and severity proportionate to AIs capability.

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Could Super Artificial Intelligence Be, in Some Sense ...

What is Artificial Intelligence (AI)?

The emergence of artificial intelligence (AI) has played a key part in ushering in the Fourth Industrial Revolution. According to the World Economic Forum, it is disrupting almost every industry in every country.

We stand on the brink of a technological revolution that will fundamentally alter the way we live, work, and relate to one another. In its scale, scope, and complexity, the transformation will be unlike anything humankind has experienced before. Klaus Schwab,Founder and Executive Chairman, World Economic Forum Geneva

Artificial intelligence is a conglomeration of concepts and technologies that means different things to different people self-driving cars, robots that impersonate humans, machine learning, and more and its applications are everywhere you look. The typical definition of AI looks something like this:

Source: KDNuggets

Jeremy Achin, CEO of DataRobot, defines AI more simply:

An AI is a computer system that is able to perform tasks that ordinarily require human intelligence. These artificial intelligence systems are powered by machine learning. Many of them are powered by machine learning, some of them are powered by specifically deep learning, some of them are powered by very boring things like just rules.

For a more in-depth explanation, watch Jeremys keynote on the subject from the Japan AI Experience.

Artificial intelligence systems are critical for companies that wish to extract value from data by automating and optimizing processes or producing actionable insights. Artificial intelligence systems powered by machine learning enable companies to leverage large amounts of available data to uncover insights and patterns that would be impossible for any one person to identify, enabling them to deliver more targeted, personalized communications, predict critical care events, identify likely fraudulent transactions, and more.

Harvard Business Review gives key insight into how important AI is in todays economic environment:

The effects of AI will be magnified in the coming decade, as manufacturing, retailing, transportation, finance, healthcare, law, advertising, insurance, entertainment, education, and virtually every other industry transform their core processes and business models to take advantage of machine learning.

Companies that fail to adopt AI and machine learning technologies are fated to be left behind:

DataRobot was founded on the belief that emerging AI and machine learning technologies should be available to all enterprises, regardless of size and resources. Thats why we invented automated machine learning, which allows users of all skill levels to easily and rapidly build and deploy machine learningmodels.

DataRobot believes in the democratization of AI, and for that reason, we developed a platform to enable business users across organizations to gain actionable, practical insights that result in tangible business value. DataRobot makes the power of AI accessible to users throughout your business, helping your organization transform into an AI-driven enterprise.

Delivering ROI for your artificial intelligence initiatives

Originally posted here:
What Is Artificial Intelligence | Artificial Intelligence Wiki

The award recognizes two of Veritones innovative AI solutions, IDentify and Redact

Veritone Inc. (NASDAQ: VERI), the creator of the worlds first operating system for artificial intelligence, aiWARE, today announced that Business Intelligence Group has named Veritone as a winner in the 2020 Artificial Intelligence Excellence Awards program for its applications Veritone IDentify and Veritone Redact, both of which are powered by Veritones aiWARE platform.

"We are honored to be recognized as a top provider of AI solutions, particularly for our turnkey applications for law enforcement agencies, IDentify and Redact," said Jon Gacek, Head of Government, Legal and Compliance at Veritone. "For the last several years, our team has worked hard to provide innovative tools to accelerate workflows, save costs and valuable resources, and protect the public. This award aligns with the traction we are getting in the market and represents a huge milestone and great recognition for our team. We are excited to continue to bring innovative, pragmatic AI solutions to the table for these important sectors."

The Artificial Intelligence Excellence Awards program recognizes the organizations, products and people who bring AI to life and apply it to solve real problems. Nominations were received and winners were chosen in four categories of AI, including Reactive Machines, Limited Memory, Theory of Mind and Self-Awareness.

"We are proud to name Veritone as a winner in our inaugural Artificial Intelligence Excellence Awards program," said Maria Jimenez, chief nominations officer for Business Intelligence Group. "It was clear to our judges that Veritone was using AI to improve the lives of their customers and employees. Congratulations to the entire team!"

Released in 2019, Veritone IDentify and Veritone Redact lower the barrier to access cutting-edge AI, enabling sectors like law enforcement and government to realize the power of the technology. Taken together, these products empower public safety by accelerating the identification of potential criminal suspects and protecting the privacy of civilians.

Veritone IDentify is a purpose-built solution that allows law enforcement teams to more quickly and accurately identify perpetrators of crimes. This application enables law enforcement agencies to easily upload still images and video of suspects in evidence footage and compare those images with thousands of known offenders in their existing criminal databases. The application gives investigators a list of potential matches for further investigation dramatically increasing the efficiency of a process that has traditionally been very manual and time-consuming. And through aggregation, filtering and selective sharing capabilities, IDentify empowers the next level of inter- and intra-agency collaboration.

As the volume of video footage collected by law enforcement and disclosed to defendants and in public information requests continues to increase, Veritone Redact ensures the privacy of innocent civilians isnt compromised in the process. Redact automates the process of obscuring both sensitive imagery and audio in video evidence, reducing the time required to redact these files by up to 90% compared with existing manual, frame-by-frame evidence redaction workflows. With automated face and head detection, as well as automatic tracking of manually selected sensitive imagery, Veritone Redact tackles massive volumes of audio and video content at incredible speed and efficiency freeing up valuable resources and helping agencies comply with privacy and disclosure laws quickly and efficiently.

For more information on Veritones solutions for government and law enforcement, please visit https://www.veritone.com/solutions/government/. To learn more about how AI-powered technologies can help police departments efficiently redact audio and video evidence collected in the field, join our webinar with Microsoft and Pemberton Township Police Departments Chief David Jantas at 1 p.m. EDT on April 2, 2020.

About Veritone

Veritone (NASDAQ: VERI) is a leading provider of artificial intelligence (AI) technology and solutions. The companys proprietary operating system, aiWARE, powers a diverse set of AI applications and intelligent process automation solutions that are transforming both commercial and government organizations. aiWARE orchestrates an expanding ecosystem of machine learning models to transform audio, video, and other data sources into actionable intelligence. The company's AI developer tools enable its customers and partners to easily develop and deploy custom applications that leverage the power of AI to dramatically improve operational efficiency and unlock untapped opportunities. Veritone is headquartered in Costa Mesa, California, and has offices in Denver, London, New York, San Diego, and Seattle. To learn more, visit Veritone.com.

Story continues

About Business Intelligence Group

The Business Intelligence Group was founded with the mission of recognizing true talent and superior performance in the business world. Unlike other industry award programs, these programs are judged by business executives having experience and knowledge. The organizations proprietary and unique scoring system selectively measures performance across multiple business domains and then rewards those companies whose achievements stand above those of their peers.

Safe Harbor Statement

This news release contains forward-looking statements, including without limitation statements regarding the features of Veritones IDentify and Redact applications, and the expected benefits to customers. Without limiting the generality of the foregoing, words such as "may," "will," "expect," "believe," "anticipate," "intend," "could," "estimate" or "continue" or the negative or other variations thereof or comparable terminology are intended to identify forward-looking statements. In addition, any statements that refer to expectations, projections or other characterizations of future events or circumstances are forward-looking statements. Assumptions relating to the foregoing involve judgments and risks with respect to various matters which are difficult or impossible to predict accurately and many of which are beyond the control of Veritone. Certain of such judgments and risks are discussed in Veritones SEC filings. Although Veritone believes that the assumptions underlying the forward-looking statements are reasonable, any of the assumptions could prove inaccurate and, therefore, there can be no assurance that the results contemplated in forward-looking statements will be realized. In light of the significant uncertainties inherent in the forward-looking information included herein, the inclusion of such information should not be regarded as a representation by Veritone or any other person that their objectives or plans will be achieved. Veritone undertakes no obligation to revise the forward-looking statements contained herein to reflect events or circumstances after the date hereof or to reflect the occurrence of unanticipated events.

View source version on businesswire.com: https://www.businesswire.com/news/home/20200327005079/en/

Contacts

Veritone Contact Allison ZulloWalker Sands, for Veritone312-241-1474veritone@walkersands.com

Business Intelligence Group Contact Maria Jimenez+1 909-529-2737jmaria@bintelligence.com

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Veritone Wins 2020 Artificial Intelligence Excellence Awards

Crazy singularities, robot rebellions, falling in love with computers: Artificialintelligence conjures up a multitude of wild what-ifs. But in the real world, A.I. involves machine learning, deep learning, and many other programmable capabilities that were just beginning to explore. Lets put the fantasy stuff on hold (at least for now) and talk about real-world A.I. Heres what it is, how it works, and where its going.

A.I. seeks to process and respond to data much like a human would. That may seem overly broad, but it needs to be: Developers are baking in human-like smarts into a wide variety of applications. Generally, A.I. falls within three categories which we would note there is still some disagreement as to what the exact definitions are, much less if theyre truly possible.

A.I. can also be classified by how it operates, which is particularly important when considering how complex an A.I. system is and its ultimate cost. If a company is creating an A.I. solution, the first question must be, Will it learn through training or inference?

As weve noted earlier, these definitions are only meant as a general guide (this Medium article is a great discussion on what weve just talked about), and some may have slightly different descriptions. But there are examples of current A.I. which are worth discussing.

Jan Schnorr/C2Sense

Voice assistants: Siri, Cortana, Alexa, and other voice assistants are growing more common, becoming the face of modern A.I. A growing subset here are chatbots, which manage messaging on websites and carry on online conversations.

Translation: This isnt just about translating language. Its also about translating objects, pictures, and sounds into data that can then be used in various algorithms.

Predictive systems: These A.I.s look at statistical data and form valuable conclusions for governments, investors, doctors, meteorologists, and nearly every other field where statistics and event prediction prove valuable.

Marketing: These A.I.s analyze buyers and their behavior, then choose tactics, products, and deals that best fit said behavior. There is a lot of crossover between these behind-the-scenes tools and voice assistants at the moment.

Research: Research A.I.s like Iris search through complex documents and studies for specific information, typically at higher speeds than Googles search engine.

Awareness: These A.I.s watch for and report unusual events when humans cant have an eye on them. One of the most complex examples of this is theft detection, which reports unusual behavior. A more exciting example, however, is self-driving cars, which use A.I. systems to scan for dangers and choose the appropriate course of action.

Editing software: These basic A.I.s look at pictures or text and locate ways that they could be improved.

Recently, neural networking expert Charles J. Simon recently opined on our pages about where he thinks A.I. is headed, which we recommend you read. While we wont cut and paste the entire article here, well point you to one specific section:

Most people look at the limitations of todays A.I. systems as evidence that AGI [general A.I.] is a long way off. We beg to differ. A.I. has most of AGIs needed pieces already in play, they just dont work together very well yet.

This is a key point. As weve noted, A.I. is getting better at least perceptually by the fact that developers are stringing together several narrow A.I. platforms. But the platforms dont talk with each other. For example, while Alexa might now be able to start your car, it cant use the current weather conditions to adjust your cars heater or air conditioning systems or start the defroster to make sure youre ready to go as soon as you get in. But Simon argues that we may have the computational and developmental capability either already and dont know it yet, or within the next decade.

Companies are spending massive amounts on money on A.I.right now, and as long as theyre willing to spend the billions (if not eventually trillions) to advance the technology, things are going to move quickly. But there are all kinds of roadblocks in the way whether it be a recessionary economy, computational challenges, and even moral and philosophical hurdles to overcome so the road to a real-world Skynet might be a long one.

While we keep coming back to the obvious Skynet references, its time for a bit of a reality check. A.I.s are long strings of programmed responses and collections of data right now, and they dont have the ability to makes trulyindependent decisions. That being the case, malice is definitely off the table for the time being. But thats not to say human error could make them so.

For example, if a predictive A.I. tells a team that storms will spawn on the East Coast next week, the team can send resources and warnings there in preparation. But if storms actually appearin the Gulf of Mexico and hit the coast there, that prediction was inaccurate and may have endangered lives. No one would think the A.I. is somehow personally to blame for this; instead, they would look at the various data inputs and algorithm adjustments. Like other types of software, A.I.s remain complex tools for people to use.

At least for now, A.I. is, for the most part, harmless and if anything helpful to the world at large. But that could change in the distant future, and at that time well need to have a serious discussion on just how much of our lives were willing to turn over to machines.

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What Is Artificial Intelligence? A.I. and Machine Learning ...

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Artificial Intelligence (AI) has made a huge impact across several industries, such as healthcare, finance, telecommunication, business, education, etc., within a short period. Today, almost every company is looking for AI professionals to implement Artificial Intelligence in their systems and provide better customer experience, along with other features. In this Artificial Intelligence Interview Questions blog, we have compiled a list of some of the most frequently asked questions by interviewers during AI-based job interviews:

Q1. What is the difference between Strong Artificial Intelligence and Weak Artificial Intelligence?Q2. What is Artificial Intelligence?Q3. List some applications of AI.Q4. List the programming languages used in AI.Q5. What is Tower of Hanoi?Q6. What is Turing test?Q7. What is an expert system? What are the characteristics of an expert system?Q8. List the advantages of an expert system.Q9. What is an A* algorithm search method?Q10. What is a breadth-first search algorithm?

The Artificial Intelligence Interview Questions blog is widely categorized in the following three types:1. Basic

2. Intermediate

3. Advanced

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Artificial Intelligence is a field of computer science wherein the cognitive functions of the human brain are studied and tried to be replicated on a machine/system. Artificial Intelligence is today widely used for various applications like computer vision, speech recognition, decision-making, perception, reasoning, cognitive capabilities, and so on.

Tower of Hanoi is a mathematical puzzle that shows how recursion might be utilized as a device in building up an algorithm to take care of a specific problem. Using a decision tree and a breadth-first search (BFS) algorithm in AI, we can solve the Tower of Hanoi.

Go through theArtificial Intelligence Course in Londonto get a clear understanding of Artificial Intelligence!

The Turing test is a method to test a machines ability to match the human-level intelligence. A machine is used to challenge human intelligence, and when it passes the test it is considered intelligent. Yet a machine could be viewed as intelligent without sufficiently knowing how to mimic a human.

An expert system is an Artificial Intelligence program that has expert-level knowledge about a specific area and how to utilize its information to react appropriately. These systems have the expertise to substitute a human expert. Their characteristics include:

A* is a computer algorithm that is extensively used for the purpose of finding the path or traversing a graph in order to find the most optimal route between various points called the nodes.

A breadth-first search (BFS) algorithm,used for searching tree or graph data structures, starts from the root node, then proceeds through neighboring nodes, and further moves toward the next level of nodes.

Till the arrangement is found, it produces one tree at any given moment. As this pursuit can be executed utilizing the FIFO (first-in, first-out) data structure, this strategy gives the shortest path to the solution.

Depth-first search (DFS) is based on LIFO (last-in, first-out). A recursion is implemented with LIFO stack data structure. Thus, the nodes are in a different order than in BFS. The path is stored in each iteration from root to leaf nodes in a linear fashion with space requirement.

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In a bidirectional search algorithm, the search begins in forward from the beginning state and in reverse from the objective state. The searches meet to identify a common state. The initial state is linked with the objective state in a reverse way. Each search is done just up to half of the aggregate way.

The repetitive search processes of level 1 and level 2 happen in this search. The search processes continue until the solution is found. Nodes are generated until a single goal node is created. Stack of nodes is saved.

The uniform cost search performs sorting in increasing the cost of the path to a node. It expands the least cost node. It is identical to BFS if each iteration has the same cost. It investigates ways in the expanding order of cost.

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AI system uses game theory for enhancement; it requires more than one participant which narrows the field quite a bit. The two fundamental roles are as follows:

AlphaBeta pruning is asearch algorithm that tries to reduce the number of nodes that are searched by the minimax algorithm in the search tree. It can be applied to n depths and can prune the entire subtrees and leaves.

Fuzzy logic is a subset of AI; it is a way of encoding human learning for artificial processing. It is a form of many-valued logic. It is represented as IF-THEN rules.

A problem has to be solved in a sequential approach to attain the goal. The partial-order plan specifies all actions that need to be undertaken but specifies an order of the actions only when required.

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First-order predicate logic is a collection of formal systems, where each statement is divided into a subject and a predicate. The predicate refers to only one subject, and it can either modify or define the properties of the subject.

Deep Learning is a subset of Machine Learning which is used to create an artificial multi-layer neural network. It has self-learning capabilities based on previous instances, and it provides high accuracy.

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Linear and logistic regression, support vector machine, and Naive Bayes

Flexibility, power, and performance

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Naive Bayes Machine Learning algorithm is a powerful algorithm for predictive modeling. It is a set of algorithms with a common principle based on Bayes Theorem. The fundamental Naive Bayes assumption is that each feature makes an independent and equal contribution to the outcome.

Perceptron is an algorithm that is able to simulate the ability of the human brain to understand and discard; it is used for the supervised classification of the input into one of the several possible non-binary outputs.

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Ensemble learning is a computational technique in which classifiers or experts are strategically formed and combined. It is used to improve classification, prediction, function approximation, etc. of a model.

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A hash table is a data structure that is used to produce an associative array which is mostly used for database indexing.

Regularization comes into the picture when a model is either overfit or underfit. It is basically used to minimize the error in a dataset. A new piece of information is fit into the dataset to avoid fitting issues.

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Model accuracy, a subset of model performance, is based on the model performance of an algorithm. Whereas, model performance is based on the datasets we feed as inputs to the algorithm.

F1 score is the weighted average of precision and recall. It considers both false positive and false negative values into account. It is used to measure a models performance.

A recommendation system is an information filtering system that is used to predict user preference based on choice patterns followed by the user while browsing/using the system.

Dimensionality reduction is the process of reducing the number of random variables. We can reduce dimensionality using techniques such as missing values ratio, low variance filter, high correlation filter, random forest, principal component analysis, etc.

Bias error is used to measure how much on an average the predicted values vary from the actual values. In case a high-bias error occurs, we have an under-performing model.

Variance is used to measure how the predictions made on the same observation differ from each other. A high-variance model will overfit the dataset and perform badly on any observation.

TensorFlow is an open-source Machine Learning library. It is a fast, flexible, and low-level toolkit for doing complex algorithms and offers users customizability to build experimental learning architectures and to work on them to produce desired outputs.

TensorFlow Installation Guide:

CPU : pip install tensorflow-cpu

GPU : pip install tensorflow-gpu

A cost function is a scalar function that quantifies the error factor of the neural network. Lower the cost function better the neural network. For example, while classifying the image in the MNIST dataset, the input image is digit 2, but the neural network wrongly predicts it to be 3.

Number of epochs:The number of times the entire training data is fed to the network while training is referred to as the number of epochs. We increase the number of epochs until the validation accuracy starts decreasing, even if the training accuracy is increasing (overfitting).

As we add more and more hidden layers, backpropagation becomes less useful in passing information to the lower layers. In effect, as information is passed back, the gradients begin to vanish and become small relative to the weights of the network.

Dropout is a simple way to prevent a neural network from overfitting. It is the dropping out of some of the units in a neural network. It is similar to the natural reproduction process, where the nature produces offsprings by combining distinct genes (dropping out others) rather than strengthening the co-adapting of them.

Long short-term memory (LSTM) is explicitly designed to address the long-term dependency problem, by maintaining a state of what to remember and what to forget.

An autoencoder is basically used to learn a compressed form of the given data. A few applications of an autoencoder are given below:

Components of GAN:

Deployment Steps:

Gradient descent is an optimization algorithm that is used to find the coefficients of parameters that are used to reduce the cost function to a minimum.

Step 1: Allocate weights (x,y) with random values and calculate the error (SSE)

Step 2: Calculate the gradient, i.e., the variation in SSE when the weights (x,y) are changed by a very small value. This helps us move the values of x and y in the direction in which SSE is minimized

Step 3: Adjust the weights with the gradients to move toward the optimal values where SSE is minimized

Step 4: Use new weights for prediction and calculating the new SSE

Step 5: Repeat Steps 2 and 3 until further adjustments to the weights do not significantly reduce the error

Syntax: Class Session

It is a class for running TensorFlow operations. The environment is encapsulated in the session object wherein the operation objects are executed and Tensor objects are evaluated.

TensorFlow cluster is a set of tasks that participate in the distributed execution of a TensorFlow graph. Each task is associated with a TensorFlow server, which contains a master that can be used to create sessions and a worker that executes operations in the graph. A cluster can also be divided into one or more jobs, where each job contains one or more tasks.

To use HDFS with TensorFlow, we need to change the file path for reading and writing data to an HDFS path. For example:

The intermediate tensors are tensors that are neither inputs nor outputs of theSession.run()call, but are in the path leading from the inputs to the outputs; they will be freed at or before the end of the call.

Sessions can own resources, few classes like tf.Variable, tf.QueueBase, and tf.ReaderBase, and they use a significant amount of memory. These resources (and the associated memory) are released when the session is closed, by calling tf.Session.close.

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When we first run the tf.Variable.initializer operation for a variable in a session, it is started. It is destroyed when we run the tf.Session.close operation.

Yes, logical inference can easily be solved in propositional logic by making use of three concepts:

Face verification is used by a lot of popular firms these days. Facebook is famous for the usage of DeepFace for its face verification needs.

There are four main things you must consider when understanding how face verification works:

There are many algorithms that are used for hyperparameter optimization, and following are the three main ones that are widely used:

Overfitting is a situation that occurs in statistical modeling or Machine Learning where the algorithm starts to over-analyze data, thereby receiving a lot of noise rather than useful information. This causes low bias but high variance, which is not a favorable outcome.

Overfitting can be prevented by using the below-mentioned methods:

Overfitting is avoided in neural nets by making use of a regularization technique called dropout.

By making use of the concept of dropouts, random neurons are dropped when the neural network is being trained to use the model doesnt overfit. If the dropout value is too low, it will have a minimal effect. If it is too high, the model will have difficulty in learning.

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