Category Archives: Artificial Intelligence

When an MIT researcher discovered that most facial recognition systems are less accurate with the faces of women and people of color, it brought to the forefront the issue of bias in technology that increasingly runs our society.Described byVarietyas a thought-provoking wake-up call,Coded Bias, a documentary film on the issue, will be presented in a special online screening bythe Appalachian Theatre of the High Country at 4 p.m. on Sunday, September 20 and will be followed by an online taped discussion with the films director, Shalini Kantayya. Please note the change in start time from the previous announcement about the documentary film series.

Coded Bias is the first event presented by BOONE DOCS, the newly-launched year-round film series at the App Theatre featuring documentary films that spark community conversation by presenting an independent lens to view our world. Showcasing emerging and award-winning filmmakers and distinct perspectives from across the globe, BOONE DOCS celebrates the creative power of independent film.

Artificial Intelligence (AI) technology is becoming more prevalent in areas that can profoundly impact peoples lives such as law enforcement and human resources. The film focuses on the work of three female mathematicians and data scientists Joy Buolamwini, Deborah Raji, and Timnit Gebru who research the implications of racial and gender bias in the cutting-edge technologies of AI and machine learning. Are the biases that exist in society being unconsciously replicated in the technology? Or worse, are opaque systems being used to shield deceptive practices?

As sci-fi writers have inspired the imagination of AI developers,Coded Biasdraws from science-fiction stylistic elements to visualize concepts in this new era of big data, says Kantayya, the films director. Coded Biasaims to inspire communities to spark new conversations about bias in the algorithms that impact civil liberties and democracy.

This event is made possible through the Southern Circuit Tour of Independent Filmmakers, a South Arts program. Since its inception in 1975, Southern Circuit has brought some of best independent filmmakers and their films from around the country to communities throughout the South. The program is supported by the National Endowment for the Arts.

In response to the COVID-19 pandemic, the 2020-21 Southern Circuit season features a hybrid model of in-person and online screenings to prioritize the health and flexibility of Screening Partner venues and filmmakers.

Coded Bias is offered free of charge to High Country audiences through the generous support of the theatres board of trustees. To subscribe to the theatres e-list and receive a link to view this event at no cost, visit the theatres website at http://www.apptheatre.org.

About the Appalachian Theatre

The mission of the Appalachian Theatre of the High Country is to revitalize and sustain this historic community touchstone as a quality home for diverse artists and audiences with a special focus on programs that celebrate our distinctive Appalachian heritage and enhance our capacity to serve as an economic catalyst for Boone and the High Country. Once a gorgeous 999-seat Art Deco movie house, the building closed in 2007 and sat empty and gutted for years. On October 14, 2019, the Appalachian Theatre re-opened its doors after a $10 million renovation that brought the distinctive Art Deco details back to this historic theatre and created a new 629-seat, state-of-the-art, acoustically fabulous venue for live concerts, films, plays, and dance performances. The historic Appalachian Theatre has entertained regional audiences in the heart of downtown Boone, North Carolina since 1938. http://www.apptheatre.org

About South Arts

South Arts advances Southern vitality through the arts. The nonprofit regional arts organization was founded in 1975 to build on the Souths unique heritage and enhance the public value of the arts. South Arts work responds to the arts environment and cultural trends with a regional perspective. South Arts offers an annual portfolio of activities designed to support the success of artists and arts providers in the South, address the needs of Southern communities through impactful arts-based programs, and celebrate the excellence, innovation, value and power of the arts of the South. For more information, visitwww.southarts.org.

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Filmmaker Reveals the Hidden Inequalities of Artificial Intelligence in Coded Bias in Online Screening by the Appalachian Theatre Sunday - High...

Technology is the future, the phrase no more represents the future. Technology has already invaded humans everyday life through various applications of Artificial Intelligence (AI). Henceforth, innovations are ruling the world today.

Artificial Intelligence (AI)has spread its wings across various sectors. The technology is making all the pointers in the impossible bucket list possible. AI refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic human actions. AI makes it a reality for machines to learn from experience, adjust to new inputs and perform numerous tasks. It can aid in building a wide-ranging branch of computer science with smart machine capabilities that perform tasks like humans.

However, AI cant anticipate the entire field as a solo performer. It has held hands with modern emerging technologies like IoT, cloud computing, machine learning (ML), big data, etc. This technological invasion will soon take the market growth to an unprecedented height. Researchers in AI sector predict that the industry will balloon toUS$190 billion by 2025.

This prediction is not surprising because AI is around humans and is almost taking humans through their daily life. Starting from automated locks at the door to smartwatches and intuitive TVs, AI is everywhere. Areportunravels that around 29% of the companies have reported using AI for their operations in the healthcare sector. It is not the only industry that is taking the path of AI. The article further establishes top sectors that are slowly turning automatic but will see extreme digitisation in the next five years.

AI and its mechanism was already a soothing breakthrough for the manufacturing sector. However, the pandemic has made it more than possible for industries to switch to AI-powered robotics. Some of the already existingAI features in factoriesare drone cameras and computer vision. Other basic automatic functions like transporting or shifting goods through AI mechanism was taking place. Henceforth, the manufacturing sector is soon looking forpredictive and analytical sensorsthat will allow them to do more complex work. One thing for sure is that this will minimise the human hand in the sector.

Security is a major threat to development in technology. Hard configurations by organisations are turning to be an easy break-in spot forhackers and cyber thieves. We cant control the cyberattacks anymore as hackers of well aware of the parallels in it. However, one thing that is still in safe hands is the invasion ofAI technology to power protection. It is estimated that companies will adopt security solutions that have been amplified with AI technology to continue to safeguard data and reinforce network security. Apart from VPN for protection on devices technology can lean on AI for added protection.

Self-driving cars are the dream of tech researchersand scientists. They are working impulsively to reach that goal. But it is not an easy or short path. It takes a lot of time and preparation before launching a safe self-driving car that signals accidents and protects the passengers from it. Today, the motor industry has voice and gesture recognition systems implanted in some high-end cars. Deep learning provided data will be able to process different types of road conditions and situations to beadded to the automatic car with the help of AI. Soon, the tech industrys long dream will come true.

Healthcare was an important industry that involves a lot of technologies like lasers and laparoscopy. However, the AI implementation is highly increased after Covid-19 pandemic. AlreadyAI is capable of detecting cancersand other symptoms that can do patients risk assessments. Now, the sudden pump ofAI in healthcare due to the compelling situationhas made many accelerating developments. Soon, wearable like watches will monitor patients with chronic illnesses to give more personalised patient care and treatment.

Marketing is a fast sector where everything happens in a short time.AI involvement in this speed marketwill make it ultra-quick. But some features need to be at place other than just AI. Better internet, robust connectivity and gadgets with quicker computer processing are essential. Now, thesupply chain and market monitoringare the general spots where AI functions. However, in the future, we can expect to see more profound changes, personalised materials and customized recommendations with the aid of AI software for marketing.

Schools are alreadyturning digital. Instead of notebooks, a good number of educational institutions are providing with digital devices to make everything online. But eh future that the sector is expecting is a virtual tutor assisting learners.AI assistants will find the students emotionby leveraging facial analysis. This will soon unravel the possibilities of better understanding between students and virtual teachers.

Journalism is a vast field. It involves reporters going to dangerous spots to capture the moment and deliver it to the audience. But harnessingAI in media will make the job easy. Bloombergs usage of Cyborg is widely being acknowledged as a success. It is no wonder if other media houses take the step and initiate similar technologies. Field reporting will also be riskless if robots are assigned for the task.

What people hate the most is waiting. However, customer services are known for making unprecedented delays. Butchatbotsandvirtual assistants come for customersaid in such situations. For the future changes, Google is working on an AI assistant that could make human-like phone calls to make appointments on humans behalf.

Banking as a long is in the past. Internet banking and ATM facilities have minimised the customers time on long queues at banks. The shifting of otherbanking processes onlinehas cut short the human labour involved in the banks front and middle office. In the future, AI is expected to predict risks and guide trends in the banking sector. AI can even makefinancial choices on behalf of humansand might act as a financial advisor.

The basic need of every human is food. The revolution of technology has made rural basins a desert as people migrate for a better life to urban society. The things left behind are the ageing farmers and empty lands. However,AI as it usually minimises human handin any other sector could come for rescue. General work like overviewing and pesticide spraying can be done by drone cameras and robots. Usingcameras to look after the cropswill help in early detection of diseases. Another initiative through AI technology is automatic vertical gardening that can even be adopted in urban areas.

Artificial Intelligence (AI) is slowly accelerating major sectors like healthcare, marketing, banking, agriculture, etc towards development with its emerging technologies and new possibilities. If the adoption continues at the same pace, within a period of five-year, all the above mentioned future possibilities will already be a reality.

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10 Amazing Artificial Intelligence Revisions to Look for in Five years - Analytics Insight

The ' Computer Vision in Artificial Intelligence (AI) market' study Added by Market Study Report, LLC, delivers an in-depth outline regarding the powerful trends existing within the industry. The study also comprises significant information concerning growth prospects, growth dynamics, market share, market size and revenue estimation of this business vertical. The report further features highlight key challenges and growth opportunities faced by the contenders of this industry, as well as enlightens the current competitive setting and growth plans enforced by the Computer Vision in Artificial Intelligence (AI) market players.

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Table of Contents:

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Computer Vision in Artificial Intelligence (AI) Market Expected to Witness High - GroundAlerts.com

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Artificial Intelligence In Food Beverages Market Top Key Players are:

Few Of The Major Competitors Currently Working In Global Artificial Intelligence In Food & Beverages Market Are Tomra System Asa, Greefa, Honeywell International Inc., Martec Of Whitell Ltd. Sesotec Gmbh,.,Key Technology Inc., Raytec Vision Spa, Rockwell Automation, Abb Ltd., Foodable Network, Llc. Startup Creator, Compac Sorting Equipment, Agco Corporation, National Recovery Technologies, Llc, Max-Ai, Buhler Ag|, Qualysense Ag, Bratney Companies, Bomill Ab, Milltec Clarfai, Inc., Bbc Technologies, Intelligentx Brewing Co.,

Global Artificial Intelligence In Food & Beverages Market Is Driven By Increasing Adoption Of Smart Devices In The Food & Beverage Sector Which Is Projecting A Rise In Estimated Value From Usd 6,385.64 Million In 2018 To An Estimated Value Of 115,397.92Million By 2026, Registering A Cagr Of 43.59% In The Forecast Period Of 2019-2026.

Artificial Intelligence In Food Beverages Market Report Overview:

Market reports are important when it comes to understanding the competitions and trends floating throughout the market. This PR on the global Artificial Intelligence In Food Beverages market contains and covers all the important aspects for the market players. It is of paramount importance to understand the technologies used and applications of the products followed the innovations in the field like the discovery of eco-friendly raw materials, which helps in adopting the latest technologies and competing the market players. In addition, this report also covers the in-depth study of the major market players working strategies to understand the cultures.

Artificial Intelligence In Food Beverages Market Competition:

The global Artificial Intelligence In Food Beverages market has become a crowded place leading to an increase in competition. This report unfolds various aspects of the market in terms of the key market players, rapidly growing players, and understanding the strategies employed by these market toppers.

Artificial Intelligence In Food Beverages Market Dynamics:

Understanding the market is of paramount importance when establishing the new business or expanding it from local to global levels. The global Artificial Intelligence In Food Beverages market report emphasizes the driver & restraints, competition, new trends, opportunities, and other factors to unfold this markets aspects and understand them. This is followed by a detailed report on the research & development programs, which helps get the details about the latest trends and upcoming technologies. All these points will help in surviving the competition and getting a better stance until the next survey.

Artificial Intelligence In Food Beverages Market Segmentation:

The global Artificial Intelligence In Food Beverages market is growing at global levels at unstoppable speeds, which has increased the demands for a better understanding of the regional markets. This report contains a detailed overview of the major global markets in American, Europe, Asia Pacific, and The Rest of the world regions. It explains the major factors helping the market players to invest smartly in the regions with maximum opportunities and potentials. This study also contains a detailed explanation of the changing government regulations in local and regional markets.

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Fundamentals of Table of Content: Artificial Intelligence In Food Beverages Market

1 Report Overview1.1 Study Scope1.2 Key Market Segments1.3 Players Covered1.4 Market Analysis by Type1.5 Market by Application1.6 Study Objectives1.7 Years Considered

2 Global Growth Trends2.1 Artificial Intelligence In Food Beverages Market Size2.2 Artificial Intelligence In Food Beverages Growth Trends by Regions2.3 Industry Trends

3 Market Share by Key Players3.1 Artificial Intelligence In Food Beverages Market Size by Manufacturers3.2 Artificial Intelligence In Food Beverages Key Players Head office and Area Served3.3 Key Players Artificial Intelligence In Food Beverages Product/Solution/Service3.4 Date of Enter into Artificial Intelligence In Food Beverages Market3.5 Mergers & Acquisitions, Expansion Plans

4 Breakdown Data by Product4.1 Global Artificial Intelligence In Food Beverages Sales by Product4.2 Global Artificial Intelligence In Food Beverages Revenue by Product4.3 Artificial Intelligence In Food Beverages Price by Product

5 Breakdown Data by End User5.1 Overview5.2 Global Artificial Intelligence In Food Beverages Breakdown Data by End User

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Artificial Intelligence In Food Beverages Market Competitive Analysis And Top Profiling Forecasts Till 2026 Tomra System Asa, Greefa, Honeywell...

Medicine-

On September 18th The Lancet Digital Health released an article called Artificial intelligence in COVID-19 drug repurposing (written by Yadi Zhou, PhD, Prof Fei Wang, PhD, Prof Jian Tang, PhD, Prof Ruth Nussinov, PhD, Prof Feixiong Cheng, PhD) in the article it explained how artificial intelligence is being used for drug reproposing ,which is where an already existing is drug used to fight novel diseases such as COVID-19.

Artificial intelligence is being used to speed up this process, with the exponential growth in computing power, memory storage and a plethora of data its only right for the medical sector to use this to speed up a process that help fight against the worlds latest threat that is COVID-19.

So how is artificial intelligence being used to speed up this process, well the article explains how artificial intelligence used for extracting hidden patterns and evidence from biomedical data, which otherwise would have been done manually saving a considerable amount of time.

Ethics-

In connection to the medical sector, artificial intelligence in medicine may be racially biased. Just like explained above artificial intelligence has transformed the healthcare and has really cut the time on many aspects of medicine such as making a breast or lung cancer diagnosis based on imaging studies, or deciding when patients should be discharged in a matter of second which is just incredible, but like all good things it comes with its flaws. During the pandemic we are currently in some researchers are raising question if these technologies may be contributing to the high rates of infection and death among the African-Americans studies show that African-Americans between the ages of 35 to 44 that sadly become infected with COVID-19 have a mortality rate 9 times higher than white Americans. As we all probably remember during the early stages of the pandemic there was very few African-Americans that contracted Covid-19, Public health officials in states such as California, Illinois, Tennessee, and Texas have said that decisions about whom and where to test was data-driven. The early focus on prosperous White communities enabled thousands of unknowingly infected people in less affluent areas to quickly spread across cities whose citizens suffer extremely high rates of underlying health conditions that create more serious and critical cases of Covid-19.

This may be the cause of the high rates of mortality among the African-American communities due to the bias of artificial intelligence from the very beginning these technologies should be verified by a range of different backgrounds before being deployed into the real world .

Autonomous Vehicles-

On a lighter note, IBM and ProMare announced the launch of an autonomous trimaran research vessel called Mayflower Autonomous Ship (MAS). This Wednesday the vessel was deployed off the coast of Plymouth, England where the Mayflower also set sail for America in 1620 400 years ago. It took 2 years of construction and to perfect the AI models that would be running the vessel.

The AI models include a captain that has the ability to sense its environment and make vital decision while navigating a crewless ship.

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Some Updates in the World of AI - AI Daily

Kate MacDonald, New Zealand Government Fellow at the World Economic Forum, and Lofred Madzou, Project Lead, AI and Machine Learning at the World Economic Forum have published a report that explains how AI can benefit everyone.

According to MacDonald and Madzou, artificial intelligence can improve the daily lives of just about everyone, however, we still need to address issues such as accuracy of AI applications, the degree of human control, transparency, bias and various privacy issues. The use of AI also needs to be carefully and ethically managed, MacDonald and Madzou recommend.

As mentioned in a blog post by MacDonald and Madzou:

One way to [ensure ethical practice in AI] is to set up a national Centre for Excellence to champion the ethical use of AI and help roll out training and awareness raising. A number of countries already have centres of excellence those which dont, should.

The blog further notes:

AI can be used to enhance the accuracy and efficiency of decision-making and to improve lives through new apps and services. It can be used to solve some of the thorny policy problems of climate change, infrastructure and healthcare. It is no surprise that governments are therefore looking at ways to build AI expertise and understanding, both within the public sector but also within the wider community.

As noted by MacDonald and Madzou, the UK has established many Office for AI centers, which aim to support the responsible adoption of AI technologies for the benefit of everyone. These UK based centers ensure that AI is safe through proper governance, strong ethical foundations and understanding of key issues such as the future of work.

The work environment is changing rapidly, especially since the COVID-19 outbreak. Many people are now working remotely and Fintech companies have managed to raise a lot of capital to launch special services for professionals who may reside in a different jurisdiction than their employer. This can make it challenging for HR departments to take care of taxes, compliance, and other routine work procedures. Thats why companies have developed remote working solutions to support companies during these challenging times.

Many firms might now require advanced cybersecurity solutions that also depend on various AI and machine learning algorithms.

The blog post notes:

AI Singapore is bringing together all Singapore-based research institutions and the AI ecosystem start-ups and companies to catalyze, synergize and boost Singapores capability to power its digital economy. Its objective is to use AI to address major challenges currently affecting society and industry.

As covered recently, AI and machine learning (ML) algorithms are increasingly being used to identify fraudulent transactions.

As reported in August 2020, the Hong Kong Institute for Monetary and Financial Research (HKIMR), the research segment of the Hong Kong Academy of Finance (AoF), had published a report on AI and banking. Entitled Artificial Intelligence in Banking: The Changing Landscape in Compliance and Supervision, the report seeks to provide insights on the long-term development strategy and direction of Hong Kongs financial industry.

In Hong Kong, the use of AI in the banking industry is said to be expanding including front-line businesses, risk management, and back-office operations. The tech is poised to tackle tasks like credit assessments and fraud detection. As well, banks are using AI to better serve their customers.

Policymakers are also exploring the use of AI in improving compliance (Regtech) and supervisory operations (Suptech), something that is anticipated to be mutually beneficial to banks and regulators as it can lower the burden on the financial institution while streamlining the regulator process.

The blog by MacDonald and Madzou also mentions that India has established a Centre of Excellence in AI to enhance the delivery of AI government e-services. The blog noted that the Centre will serve as a platform for innovation and act as a gateway to test and develop solutions and build capacity across government departments.

The blog post added that Canada is notably the worlds first country to introduce a National AI Strategy, and to also establish various centers of excellence in AI research and innovation at local universities. The blog further states that this investment in academics and researchers has built on Canadas reputation as a leading AI research hub.

MacDonald and Madzou also mentioned that Malta has launched the Malta Digital Innovation Authority, which serves as a regulatory body that handles governmental policies that focus on positioning Malta as a centre of excellence and innovation in digital technologies. The island countrys Innovation Authority is responsible for establishing and enforcing relevant standards while taking appropriate measures to ensure consumer protection.

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AI and Machine Learning Technologies Are On the Rise Globally, with Governments Launching Initiatives to Support Adoption: Report - Crowdfund Insider

The common, and recurring, view of the latest breakthroughs in artificial intelligence research is that sentient and intelligent machines are just on the horizon. Machines understand verbal commands, distinguish pictures, drive cars and play games better than we do. How much longer can it be before they walk among us?

The new White House report on artificial intelligence takes an appropriately skeptical view of that dream. It says the next 20 years likely wont see machines exhibit broadly-applicable intelligence comparable to or exceeding that of humans, though it does go on to say that in the coming years, machines will reach and exceed human performance on more and more tasks. But its assumptions about how those capabilities will develop missed some important points.

As an AI researcher, Ill admit it was nice to have my own field highlighted at the highest level of American government, but the report focused almost exclusively on what I call the boring kind of AI. It dismissed in half a sentence my branch of AI research, into how evolution can help develop ever-improving AI systems, and how computational models can help us understand how our human intelligence evolved.

The report focuses on what might be called mainstream AI tools: machine learning and deep learning. These are the sorts of technologies that have been able to play Jeopardy! well, and beat human Go masters at the most complicated game ever invented. These current intelligent systems are able to handle huge amounts of data and make complex calculations very quickly. But they lack an element that will be key to building the sentient machines we picture having in the future.

We need to do more than teach machines to learn. We need to overcome the boundaries that define the four different types of artificial intelligence, the barriers that separate machines from us and us from them.

There are four types of artificial intelligence: reactive machines, limited memory, theory of mind and self-awareness.

The most basic types of AI systems are purely reactive, and have the ability neither to form memories nor to use past experiences to inform current decisions. Deep Blue, IBMs chess-playing supercomputer, which beat international grandmaster Garry Kasparov in the late 1990s, is the perfect example of this type of machine.

Deep Blue can identify the pieces on a chess board and know how each moves. It can make predictions about what moves might be next for it and its opponent. And it can choose the most optimal moves from among the possibilities.

But it doesnt have any concept of the past, nor any memory of what has happened before. Apart from a rarely used chess-specific rule against repeating the same move three times, Deep Blue ignores everything before the present moment. All it does is look at the pieces on the chess board as it stands right now, and choose from possible next moves.

This type of intelligence involves the computer perceiving the world directly and acting on what it sees. It doesnt rely on an internal concept of the world. In a seminal paper, AI researcher Rodney Brooks argued that we should only build machines like this. His main reason was that people are not very good at programming accurate simulated worlds for computers to use, what is called in AI scholarship a representation of the world.

The current intelligent machines we marvel at either have no such concept of the world, or have a very limited and specialized one for its particular duties. The innovation in Deep Blues design was not to broaden the range of possible movies the computer considered. Rather, the developers found a way to narrow its view, to stop pursuing some potential future moves, based on how it rated their outcome. Without this ability, Deep Blue would have needed to be an even more powerful computer to actually beat Kasparov.

Similarly, Googles AlphaGo, which has beaten top human Go experts, cant evaluate all potential future moves either. Its analysis method is more sophisticated than Deep Blues, using a neural network to evaluate game developments.

These methods do improve the ability of AI systems to play specific games better, but they cant be easily changed or applied to other situations. These computerized imaginations have no concept of the wider world meaning they cant function beyond the specific tasks theyre assigned and are easily fooled.

They cant interactively participate in the world, the way we imagine AI systems one day might. Instead, these machines will behave exactly the same way every time they encounter the same situation. This can be very good for ensuring an AI system is trustworthy: You want your autonomous car to be a reliable driver. But its bad if we want machines to truly engage with, and respond to, the world. These simplest AI systems wont ever be bored, or interested, or sad.

This Type II class contains machines can look into the past. Self-driving cars do some of this already. For example, they observe other cars speed and direction. That cant be done in a just one moment, but rather requires identifying specific objects and monitoring them over time.

These observations are added to the self-driving cars preprogrammed representations of the world, which also include lane markings, traffic lights and other important elements, like curves in the road. Theyre included when the car decides when to change lanes, to avoid cutting off another driver or being hit by a nearby car.

But these simple pieces of information about the past are only transient. They arent saved as part of the cars library of experience it can learn from, the way human drivers compile experience over years behind the wheel.

So how can we build AI systems that build full representations, remember their experiences and learn how to handle new situations? Brooks was right in that it is very difficult to do this. My own research into methods inspired by Darwinian evolution can start to make up for human shortcomings by letting the machines build their own representations.

We might stop here, and call this point the important divide between the machines we have and the machines we will build in the future. However, it is better to be more specific to discuss the types of representations machines need to form, and what they need to be about.

Machines in the next, more advanced, class not only form representations about the world, but also about other agents or entities in the world. In psychology, this is called theory of mind the understanding that people, creatures and objects in the world can have thoughts and emotions that affect their own behavior.

This is crucial to how we humans formed societies, because they allowed us to have social interactions. Without understanding each others motives and intentions, and without taking into account what somebody else knows either about me or the environment, working together is at best difficult, at worst impossible.

If AI systems are indeed ever to walk among us, theyll have to be able to understand that each of us has thoughts and feelings and expectations for how well be treated. And theyll have to adjust their behavior accordingly.

The final step of AI development is to build systems that can form representations about themselves. Ultimately, we AI researchers will have to not only understand consciousness, but build machines that have it.

This is, in a sense, an extension of the theory of mind possessed by Type III artificial intelligences. Consciousness is also called self-awareness for a reason. (I want that item is a very different statement from I know I want that item.) Conscious beings are aware of themselves, know about their internal states, and are able to predict feelings of others. We assume someone honking behind us in traffic is angry or impatient, because thats how we feel when we honk at others. Without a theory of mind, we could not make those sorts of inferences.

While we are probably far from creating machines that are self-aware, we should focus our efforts toward understanding memory, learning and the ability to base decisions on past experiences. This is an important step to understand human intelligence on its own. And it is crucial if we want to design or evolve machines that are more than exceptional at classifying what they see in front of them.

This article was originally published on The Conversation.

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Understanding the Four Types of Artificial Intelligence

Artificial intelligence (AI) is clearly a growing force in the technology industry. AI is taking center stage at conferences and showing potential across a wide variety of industries, including retail and manufacturing. New products are being embedded with virtual assistants, while chatbots are answering customer questions on everything from your online office supplier's site to your web hosting service provider's support page. Meanwhile, companies such as Google, Microsoft, and Salesforce are integrating AI as an intelligence layer across their entire tech stack. Yes, AI is definitely having its moment.

This isn't the AI that pop culture has conditioned us to expect; it's not sentient robots or Skynet, or even Tony Stark's Jarvis assistant. This AI plateau is happening under the surface, making our existing tech smarter and unlocking the power of all the data that enterprises collect. What that means: Widespread advancement in machine learning (ML), computer vision, deep learning, and natural language processing (NLP) have made it easier than ever to bake an AI algorithm layer into your software or cloud platform.

For businesses, practical AI applications can manifest in all sorts of ways depending on your organizational needs and the business intelligence (BI) insights derived from the data you collect. Enterprises can employ AI for everything from mining social data to driving engagement in customer relationship management (CRM) to optimizing logistics and efficiency when it comes to tracking and managing assets.

ML is playing a key role in the development of AI, noted Luke Tang, General Manager of TechCode's Global AI+ Accelerator program, which incubates AI startups and helps companies incorporate AI on top of their existing products and services.

"Right now, AI is being driven by all the recent progress in ML. There's no one single breakthrough you can point to, but the business value we can extract from ML now is off the charts," Tang said. "From the enterprise point of view, what's happening right now could disrupt some core corporate business processes around coordination and control: scheduling, resource allocation and reporting." Here we provide tips from some experts to explain the steps businesses can take to integrate AI in your organization and to ensure your implementation is a success.

Take the time to become familiar with what modern AI can do. The TechCode Accelerator offers its startups a wide array of resources through its partnerships with organizations such as Stanford University and corporations in the AI space. You should also take advantage of the wealth of online information and resources available to familiarize yourself with the basic concepts of AI. Tang recommends some of the remote workshops and online courses offered by organizations such as Udacity as easy ways to get started with AI and to increase your knowledge of areas such as ML and predictive analytics within your organization.

The following are a number of online resources (free and paid) that you can use to get started:

Once you're up to speed on the basics, the next step for any business is to begin exploring different ideas. Think about how you can add AI capabilities to your existing products and services. More importantly, your company should have in mind specific use cases in which AI could solve business problems or provide demonstrable value.

"When we're working with a company, we start with an overview of its key tech programs and problems. We want to be able to show it how natural language processing, image recognition, ML, etc. fit into those products, usually with a workshop of some sort with the management of the company," Tang explained. "The specifics always vary by industry. For example, if the company does video surveillance, it can capture a lot of value by adding ML to that process."

Next, you need to assess the potential business and financial value of the various possible AI implementations you've identified. It's easy to get lost in "pie in the sky" AI discussions, but Tang stressed the importance of tying your initiatives directly to business value.

"To prioritize, look at the dimensions of potential and feasibility and put them into a 2x2 matrix," Tang said. "This should help you prioritize based on near-term visibility and know what the financial value is for the company. For this step, you usually need ownership and recognition from managers and top-level executives."

There's a stark difference between what you want to accomplish and what you have the organizational ability to actually achieve within a given time frame. Tang said a business should know what it's capable of and what it's not from a tech and business process perspective before launching into a full-blown AI implementation.

"Sometimes this can take a long time to do," Tang said. "Addressing your internal capability gap means identifying what you need to acquire and any processes that need to be internally evolved before you get going. Depending on the business, there may be existing projects or teams that can help do this organically for certain business units."

Once your business is ready from an organizational and tech standpoint, then it's time to start building and integrating. Tang said the most important factors here are to start small, have project goals in mind, and, most importantly, be aware of what you know and what you don't know about AI. This is where bringing in outside experts or AI consultants can be invaluable.

"You don't need a lot of time for a first project; usually for a pilot project, 2-3 months is a good range," Tang said. "You want to bring internal and external people together in a small team, maybe 4-5 people, and that tighter time frame will keep the team focused on straightforward goals. After the pilot is completed, you should be able to decide what the longer-term, more elaborate project will be and whether the value proposition makes sense for your business. It's also important that expertise from both sidesthe people who know about the business and the people who know about AIis merged on your pilot project team."

Tang noted that, before implementing ML into your business, you need to clean your data to make it ready to avoid a "garbage in, garbage out" scenario. "Internal corporate data is typically spread out in multiple data silos of different legacy systems, and may even be in the hands of different business groups with different priorities," Tang said. "Therefore, a very important step toward obtaining high-quality data is to form a cross-[business unit] taskforce, integrate different data sets together, and sort out inconsistencies so that the data is accurate and rich, with all the right dimensions required for ML."

Begin applying AI to a small sample of your data rather than taking on too much too soon. "Start simple, use AI incrementally to prove value, collect feedback, and then expand accordingly," said Aaron Brauser, Vice President of Solutions Management at M*Modal, which offers natural language understanding (NLU) tech for health care organizations as well as an AI platform that integrates with electronic medical records (EMRs).

A specific type of data could be information on certain medical specialties. "Be selective in what the AI will be reading," said Dr. Gilan El Saadawi, Chief Medical Information Officer (CMIO) at M*Modal. "For example, pick a certain problem you want to solve, focus the AI on it, and give it a specific question to answer and not throw all the data at it."

After you ramp up from a small sample of data, you'll need to consider the storage requirements to implement an AI solution, according to Philip Pokorny, Chief Technical Officer (CTO) at Penguin Computing, a company that offers high-performance computing (HPC), AI, and ML solutions.

"Improving algorithms is important to reaching research results. But without huge volumes of data to help build more accurate models, AI systems cannot improve enough to achieve your computing objectives," Pokorny wrote in a white paper entitled, "Critical Decisions: A Guide to Building the Complete Artificial Intelligence Solution Without Regrets." "That's why inclusion of fast, optimized storage should be considered at the start of AI system design."

In addition, you should optimize AI storage for data ingest, workflow, and modeling, he suggested. "Taking the time to review your options can have a huge, positive impact to how the system runs once its online," Pokorny added.

With the additional insight and automation provided by AI, workers have a tool to make AI a part of their daily routine rather than something that replaces it, according to Dominic Wellington, Global IT Evangelist at Moogsoft, a provider of AI for IT operations (AIOps). "Some employees may be wary of technology that can affect their job, so introducing the solution as a way to augment their daily tasks is important," Wellington explained.

He added that companies should be transparent on how the tech works to resolve issues in a workflow. "This gives employees an 'under the hood' experience so that they can clearly visualize how AI augments their role rather than eliminating it," he said.

When you're building an AI system, it requires a combination of meeting the needs of the tech as well as the research project, Pokorny explained. "The overarching consideration, even before starting to design an AI system, is that you should build the system with balance," Pokorny said. "This may sound obvious but, too often, AI systems are designed around specific aspects of how the team envisions achieving its research goals, without understanding the requirements and limitations of the hardware and software that would support the research. The result is a less-than-optimal, even dysfunctional, system that fails to achieve the desired goals."

To achieve this balance, companies need to build in sufficient bandwidth for storage, the graphics processing unit (GPU), and networking. Security is an oft-overlooked component as well. AI by its nature requires access to broad swaths of data to do its job. Make sure that you understand what kinds of data will be involved with the project and that your usual security safeguards -- encryption, virtual private networks (VPN), and anti-malware -- may not be enough.

"Similarly, you have to balance how the overall budget is spent to achieve research with the need to protect against power failure and other scenarios through redundancies," Pokorny said. "You may also need to build in flexibility to allow repurposing of hardware as user requirements change."

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10 Steps to Adopting Artificial Intelligence in Your ...

Now is when things start getting real.

Click on "Create Intent", at the top of the console, to create you very first Intent.

I will be naming this Intent, "startconvo.Hi" (At the topmost blank), and the purpose of this intent would be to respond to greetings such as Hi, Hello, etc.

Intents have 2 main sections:

USER SAYS: In this section, you'll provide various phrases a User may ask. The more phrases you add, the better your Assistant can learn and respond to similar phrases. (Try to add at least half a dozen phrases so that your Agent can understand and can recognize other similar phrases.)

RESPONSE: Here, you'll provide answers for the said User phrases. You can add multiple responses in this section, and your Agent will pick one at random.This is done to avoid redundancy, and make the conversation more natural-like. Responses can also be rich messages like Cards, Images, etc, that are displayed in devices that support them. (Refer to docs for more info: Rich Messages)

For JARVIS this is what the 2 sections contain:

User Says : Hi, Hey, Hello, Yo

Responses : My man! , Hey! , Hi There! , Yo Dawg!

Don't forget to Save after adding changes.

YOU NOW HAVE AN AI ASSISTANT (YAAAAAAAY!!!). Try talking to it in the test console.

P.S: If you are using Chrome Browser, you can click on the mic icon in the Test Console to talk to your Agent and get your response.

P.S.2: Notice how JARVIS responds when I say "Hey Jarvis!" (or) "Hola Jarvis!" even though I haven't fed that phrase in the User says section. (It's a Magic Trick! xD)

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Build Your Own AI (Artificial Intelligence) Assistant 101 ...

Artificial intelligence (AI) is defined as a branch of computer science dealing with the simulation of intelligent behavior in computers; the capability of a machine to imitate intelligent human behavior, according to Websters Dictionary.Recent advancements in the field, however, have proven that AI is so much more than a mere scientific curiosity. The technological advances attributed to AI have the potential to completely alter the world as we know it.

Artificial intelligence used to be the stuff of science fiction;evidence of the concept being studied by real-life scientists dates back to the 1950s. The famous Alan Turing explored the theory in a research paper in the year 1950, but the fundamentals of computers werent yet advanced enough to bring the idea of artificial intelligence to fruition. By 1955 The Logic Theorist program funded by the Research and Development (RAND) Corporation became what many believe to be the first example of AI. The program was designed to mimic the human minds ability to problem solve, eventually setting the stage for a historic conference called the Dartmouth Summer Research Project on Artificial Intelligence in 1956.

As computers began to get faster, more powerful, and less expensive, AI began to pick up steam through the '70s. Successful projects began emerging in scientific communities, some even securing funding from government agencies. Then, for close to a decade, AI research hit a wall as funding lapsed and scientific theories began to outpace computer ability once again. The biggest exception was a Japanese government-funded, $400 million project aimed at improving artificial intelligence from 1982 to 1990.

The 1990s and 2000s saw some huge advancements in artificial intelligence as the fundamental limits of computer storage yielded to new hardware innovations. As the applications of AI become more and more prevalent in the daily lives of humans, it is essential to have the context of some of the most important advances in AI history.

Stacker explored 25 advances in artificial intelligence from all different uses, applications, and innovations. Whether its robots, supercomputers, health care, or search optimization, AI is coming up strong.

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25 Stunning Advances in Artificial Intelligence | Stacker