In response to Elfriede Rothenberg

Apple’s Siri, Google Now, Amazon’s Alexa, and Microsoft’s Cortana are digital assistants that help users perform various tasks, from checking their schedules and searching for something on the web, to sending commands to another app. AI is an important part of how these apps work because they learn from every single user interaction.

The Roomba 980 model vacuum (the one that cleans your floor on its own) uses AI to scan a living area’s size, look for objects that might be in the way, and remember the best route for cleaning the carpet. The vacuum bot can also identify how much cleaning it needs to do based on the size of the room, repeating a cleaning cycle three times in smaller rooms or cleaning twice in a medium-sized room.

In response to Dorothea Petrescu

Self-driving and parking cars use deep learning, a subset of AI, to recognize the space around a vehicle. Technology company Nvidia uses AI to give cars “the power to see, think, and learn, so they can navigate a nearly infinite range of possible driving scenarios,” Nvidia explains on its website. The company’s AI-powered technology is already in use in cars made by Toyota, Mercedes-Benz, Audi, Volvo, and Tesla, and is sure to revolutionize how people drive—and enable vehicles to drive themselves.

Apple’s Siri, Google Now, Amazon’s Alexa, and Microsoft’s Cortana are digital assistants that help users perform various tasks, from checking their schedules and searching for something on the web, to sending commands to another app. AI is an important part of how these apps work because they learn from every single user interaction.

In response to Gabriel Schurter

What are some examples of AI-based devices we use in our daily lives?

Self-driving and parking cars use deep learning, a subset of AI, to recognize the space around a vehicle. Technology company Nvidia uses AI to give cars “the power to see, think, and learn, so they can navigate a nearly infinite range of possible driving scenarios,” Nvidia explains on its website. The company’s AI-powered technology is already in use in cars made by Toyota, Mercedes-Benz, Audi, Volvo, and Tesla, and is sure to revolutionize how people drive—and enable vehicles to drive themselves.

What are some examples of AI-based devices we use in our daily lives?

AI algorithms are usually very complex, often requiring thousands of calculations – sometimes even more –computed every second. With the development of cloud and distributed processing over the past decade, it became possible to process such algorithms, ushering in the current age of AI-powered data analytics.

A deep learning agent is any autonomous or semi-autonomous AI-driven system that uses deep learning to perform and improve at its tasks. Systems (agents) that use deep learning include chatbots, self-driving cars, expert systems, facial recognition programs and robots.

No, tomorrow’s AI won’t live up to the hype. Freeing ordinary folks from repetitive tasks and giving them personal assistants only allows people to busy themselves with other, more complex tasks. The resulting productivity will mark incremental gains for business owners, but nothing on par with the digital revolution and the industrial one before it. For that, we’ll have to wait for the robots.

AI will save companies considerable time by doing tasks and collecting data as well as providing decisions based on that data much faster than human beings can do. It seems quite possible that AI has the capability of doing so much more than we can on many levels. It’s an exciting time to watch the changes that AI brings.

AI autopilots in commercial airlines is a  surprisingly early use of AI technology that dates as far back as 1914, depending on how loosely you define autopilot. The New York Times reports that the average flight of a Boeing plane involves only seven minutes of human-steered flight, which is typically reserved only for takeoff and landing.

Machine learning is a subset of AI. The idea is that machines will “learn” and get better at tasks over time rather than having humans constantly having to input parameters. Machine learning is a practical application of AI. 

In response to Luned Birutė Mag Raith

Data mining is the process of analyzing hidden patterns of data according to different perspectives for categorization into useful information, which is collected and assembled in common areas, such as data warehouses, for efficient analysis, data mining algorithms, facilitating business decision making and other information requirements to ultimately cut costs and increase revenue.

Data mining is also known as data discovery and knowledge discovery.

Data mining is the process of analyzing hidden patterns of data according to different perspectives for categorization into useful information, which is collected and assembled in common areas, such as data warehouses, for efficient analysis, data mining algorithms, facilitating business decision making and other information requirements to ultimately cut costs and increase revenue.

In response to Sonja Ham Mac Diarmada

A good example of machine learning implementation is Facebook. Facebook’s machine learning algorithms gather behavioral information for every user on the social platform. Based on one’s past behavior, the algorithm predicts interests and recommends articles and notifications on the News Feed. Similarly, when Amazon recommends “You might also like” products, or when Netflix recommends a movie based on past behaviors, machine learning is at work. 

Machine learning is just a different perspective on statistics. Here are critical skills that can help you carve out a career in this fast-growing domain:

• Expertise in computer fundamentals
• In depth knowledge of programming skills
• Knowledge of probability and statistics
• Data modeling and evaluation skills

A good example of machine learning implementation is Facebook. Facebook’s machine learning algorithms gather behavioral information for every user on the social platform. Based on one’s past behavior, the algorithm predicts interests and recommends articles and notifications on the News Feed. Similarly, when Amazon recommends “You might also like” products, or when Netflix recommends a movie based on past behaviors, machine learning is at work. 

A data analyst should be able to take a specific question or a specific topic and discuss what the data looks like and represent that data to relevant stakeholders in the company.  If you’re looking to step into the role of a data analyst, you must gain these four key skills:

• Knowledge of mathematical statistics
• Fluent understanding of  R and Python
• Data wrangling
• Understand PIG/ HIVE

For centuries, people have predicted that machines would make workers obsolete and increase unemployment, although the causes of unemployment are usually thought to be due to social policy.  A recent example of human replacement involves Taiwanese technology company Foxconn who, in July 2011, announced a three-year plan to replace workers with more robots. At present the company uses ten thousand robots but will increase them to a million robots over a three-year period. Lawyers have speculated that an increased prevalence of robots in the workplace could lead to the need to improve redundancy laws

Here are a few problems where AI methods can be applied: 

• Optical character recognition
• Handwriting recognition
• Speech recognition
• Face recognition

As for other fields, I think Robotics is the big one right now.  Automation and data mining are two other very hot fields at the moment.

Hi all,

I am looking for a more detailed discussion of (1) typical problems to which AI methods are applied as well as (2) other fields in which AI methods are implemented.  Any posts will be greatly appreciated.

One type of AI system is the so called reactive machines.  An example is Deep Blue, the IBM chess program that beat Garry Kasparov in the 1990s. Deep Blue can identify pieces on the chess board and make predictions, but it has no memory and cannot use past experiences to inform future ones. It analyzes possible moves -- its own and its opponent -- and chooses the most strategic move. Deep Blue and Google's Alpha.GO were designed for narrow purposes and cannot easily be applied to another situation.

AI (artificial intelligence) is the simulation of human intelligence processes by machines, especially computer systems. These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions) and self-correction. Particular applications of AI include expert systems, speech recognition and machine vision.

In response to Dan

I liked very much the example with the traffic light. The machine will see that the other cars stops on red light and will do the same. But what happens if a group of people purposely starts passing on red light and the machines start doing the same thing. Chaos :)

Yes but there can be both, you can set a certain number of rules that the AI has to obey and play around with the other variables in the environment.

The biggest problem with recovering from cyber-attacks is that security professionals rarely get the chance to deal with them immediately.  Since artificial intelligence doesn’t need to sleep, though, they can set defense systems against malware the moment it begins to download.

Developers understand artificial intelligence better than ever and how to manipulate its workings. For that reason, there’s no doubt that the future holds a bit of robot-human cooperation in defense of data everywhere.

How can a financial institution determine if a transaction is fraudulent? In most cases, the daily transaction volume is far too high for humans to manually review each transaction. Instead, AI is used to create systems that learn what types of transactions are fraudulent.  FICO, the company that creates the well-known credit ratings used to determine creditworthiness, uses neural networks to predict fraudulent transactions. Factors that may affect the neural network’s final output include recent frequency of transactions, transaction size, and the kind of retailer involved.

In response to Ingegerd Poulsen

Tomorrow’s AI won’t live up to the hype. Freeing ordinary folks from repetitive tasks and giving them personal assistants only allows people to busy themselves with other, more complex tasks. The resulting productivity will mark incremental gains for business owners, but nothing on par with the digital revolution and the industrial one before it. For that, we’ll have to wait for the robots.

Yes, but the problem is some people are not suitable for more complex tasks. Not everyone can before a doctor, or a lawyer. How can we deal with this? 

I liked very much the example with the traffic light. The machine will see that the other cars stops on red light and will do the same. But what happens if a group of people purposely starts passing on red light and the machines start doing the same thing. Chaos :)

Tomorrow’s AI won’t live up to the hype. Freeing ordinary folks from repetitive tasks and giving them personal assistants only allows people to busy themselves with other, more complex tasks. The resulting productivity will mark incremental gains for business owners, but nothing on par with the digital revolution and the industrial one before it. For that, we’ll have to wait for the robots.

In response to Baldur Helgason

Although big data provides great opportunities for a broad of areas including e-commerce, industrial control and smart medical, it poses many challenging issues on data mining and information processing due to its characteristics of large volume, large variety, large velocity and large veracity.

Data mining may lead to serious issues in terms of data security, privacy and governance. For example, when a retailer analyzes the purchase details, it reveals information about buying habits and preferences of customers without their permission.

Artificial Intelligence and Machine Learning are the hottest jobs in the industry right now. 2018 has seen an even bigger leap in interest in these fields and it is expected to grow exponentially in the next five years! For instance, did you know that more than 50,000 positions related to Data and Analytics are currently vacant in India?

Although big data provides great opportunities for a broad of areas including e-commerce, industrial control and smart medical, it poses many challenging issues on data mining and information processing due to its characteristics of large volume, large variety, large velocity and large veracity.

In response to Vincent Fournier

Hi Yoga,

I think that the great success of AlphaZero is due to the fact that it did not "assimilated hundreds of years of chess knowledge and tactics". It just started from scratch and found a complete new way to play chess, this is also why it is so successfull.

What would  be great here would be to re-train the same machine from zero to see if it found another complete new way to play it or it find the same strategies found the AlphaZero. That should be really interesting... if the same pattern is recreated, then we should conclude that there are meta-rules for playing chess that we don't see, but computer constantly finds.

 

I remember reading something about this on the web.  The one thing that I found really memorable was the fact that the computer used his King as an attacking piece!..

In response to YogaFan

Susan,

I just want to add another example here.  AlphaZero, an AI computer program, in 2017 proved itself to be the world’s greatest ever chess champion, thrashing a previous title-holder, another AI system called Stockfish 8, in a 100-game marathon.  So far, so nerdy, and possibly something only chess devotees or computer geeks might get excited about.  But what’s so frighteningly clever about AlphaZero is that it taught itself chess in just four hours. It was simply given the rules and — crucially — instructed to learn how to win by playing against itself. In doing so, it assimilated hundreds of years of chess knowledge and tactics — but then went on to surpass all previous human invention in the game.

Hi Yoga,

I think that the great success of AlphaZero is due to the fact that it did not "assimilated hundreds of years of chess knowledge and tactics". It just started from scratch and found a complete new way to play chess, this is also why it is so successfull.

What would  be great here would be to re-train the same machine from zero to see if it found another complete new way to play it or it find the same strategies found the AlphaZero. That should be really interesting... if the same pattern is recreated, then we should conclude that there are meta-rules for playing chess that we don't see, but computer constantly finds.

In response to Rosanne Ostberg

From what I understand AI (or Artificial Intelligence) is defined as the ability of a computer or a robot to perform tasks commonly associated with intelligent beings.  Can someone provide a few good examples of AI being used in real life?

Rosanne,

For example the automation infrastructure of a leather garments plant based in Bangladesh that exports its products to the entire European market will be able to judge market requirements for the coming winter season in much accurate and insightful manner if it is able to access and analyze big data reports about the market, financial and weather conditions of that area throughout the year.

In response to Susan Boil

If am not mistaken, last year at the biggest E-sports event in the world the Dota 2 international championship, a small team of Ai developers introduced an Ai robot specifically designed to play 1 versus 1: "The bot learned the game from scratch by self-play, and does not use imitation learning or the tree search. This is a step towards building Ai systems which accomplish well-defined goals in messy, complicated situations involving real himans"

Susan,

I just want to add another example here.  AlphaZero, an AI computer program, in 2017 proved itself to be the world’s greatest ever chess champion, thrashing a previous title-holder, another AI system called Stockfish 8, in a 100-game marathon.  So far, so nerdy, and possibly something only chess devotees or computer geeks might get excited about.  But what’s so frighteningly clever about AlphaZero is that it taught itself chess in just four hours. It was simply given the rules and — crucially — instructed to learn how to win by playing against itself. In doing so, it assimilated hundreds of years of chess knowledge and tactics — but then went on to surpass all previous human invention in the game.

In response to Rosanne Ostberg

From what I understand AI (or Artificial Intelligence) is defined as the ability of a computer or a robot to perform tasks commonly associated with intelligent beings.  Can someone provide a few good examples of AI being used in real life?

For example, Google’s AI-Powered Predictions: using anonymized location data from smartphones, Google Maps (Maps) can analyze the speed of movement of traffic at any given time. And, with its acquisition of crowdsourced traffic app Waze in 2013, Maps can more easily incorporate user-reported traffic incidents like construction and accidents. Access to vast amounts of data being fed to its proprietary algorithms means Maps can reduce commutes by suggesting the fastest routes to and from work.

@Lin,

Related to your comment that machines may be able to learn one day, I would like to bring up another point.  If (and that’s a big IF) we reach a stage where humanoid AI robots match human capacities (or even less intelligent animals like rats) in awareness, sentience and intelligence, we’ll have to decide if they should be granted certain rights, freedoms and protections.

If am not mistaken, last year at the biggest E-sports event in the world the Dota 2 international championship, a small team of Ai developers introduced an Ai robot specifically designed to play 1 versus 1: "The bot learned the game from scratch by self-play, and does not use imitation learning or the tree search. This is a step towards building Ai systems which accomplish well-defined goals in messy, complicated situations involving real himans"

I was really interested to hear that smart algorithms are a form of AI, as I considered AI to more about machine learning in the future. 

Computational power is what drives the evolution of AI.  From what I understand GPU’s (Graphics Processing Units) are pretty important when it comes to computational power and it seems like we started using them about a decade ago.  Here is an image of one such unit.

From what I understand AI (or Artificial Intelligence) is defined as the ability of a computer or a robot to perform tasks commonly associated with intelligent beings.  Can someone provide a few good examples of AI being used in real life?

Your explanation of what happens when we learn helped me understand how neural networks work at all. I would imagine machines may be designed to learn in a similar way.

Very interesting discussion on Deep Learning.