In response to Professor Dodds

True, these prodigious accomplishments are all in so-called narrow AI, where machines perform highly specialized tasks. But many experts believe this restriction is very temporary. By mid-century, we may have artificial general intelligence (AGI) – machines that are capable of human-level performance on the full range of tasks that we ourselves can tackle.

If so, then there’s little reason to think that it will stop there. Machines will be free of many of the physical constraints on human intelligence. Our brains run at slow biochemical processing speeds on the power of a light bulb, and need to fit through a human birth canal. It is remarkable what they accomplish, given these handicaps. But they may be as far from the physical limits of thought as our eyes are from the Webb Space Telescope.

In response to Erinda Zhulati

This has been the decade of AI, with one astonishing feat after another. A chess-playing AI that can defeat not only all human chess players, but also all previous human-programmed chess machines, after learning the game in just four hours? That’s yesterday’s news, what’s next?

True, these prodigious accomplishments are all in so-called narrow AI, where machines perform highly specialized tasks. But many experts believe this restriction is very temporary. By mid-century, we may have artificial general intelligence (AGI) – machines that are capable of human-level performance on the full range of tasks that we ourselves can tackle.

This has been the decade of AI, with one astonishing feat after another. A chess-playing AI that can defeat not only all human chess players, but also all previous human-programmed chess machines, after learning the game in just four hours? That’s yesterday’s news, what’s next?

In response to Liam Anderson

If you look 100 times for correlations between two variables, you risk finding, purely by chance, about five bogus correlations that appear statistically significant — even though there is no actual meaningful connection between the variables. Absent careful supervision, the magnitudes of big data can greatly amplify such errors.

Big data is here to stay, as it should be. But let’s be realistic: It’s an important resource for anyone analyzing data, not a silver bullet.

If you look 100 times for correlations between two variables, you risk finding, purely by chance, about five bogus correlations that appear statistically significant — even though there is no actual meaningful connection between the variables. Absent careful supervision, the magnitudes of big data can greatly amplify such errors.

As AI and big data continue disrupting industries across the board, issues related to transparency will inevitably force discussions into what people should really expect from AI-powered data analytics. For younger generations and digital natives who’ve often been said to be liberal with personal information on the internet, it’s critical for organizations that use this data to clearly outline the scope within which such data will be used, lest legal issues arise from misuse of trust.

In response to Volodya Kuznetsov

AI’s impact on marketing is growing, predicted to reach nearly $40 billion by 2025. Most CMOs are aware of AI, but many are still unsure and unaware of the magnitude of the benefits and how they can adopt AI to improve marketing.  

Volodya,

before marketers commit to and execute their AI strategy, they need to understand the opportunity and difference between data analytics, predictive analytics and AI machine learning.

AI’s impact on marketing is growing, predicted to reach nearly $40 billion by 2025. Most CMOs are aware of AI, but many are still unsure and unaware of the magnitude of the benefits and how they can adopt AI to improve marketing.  

Machine learning, data science and data analytics or scientist are emerging fields growing into various sub-fields helping companies to improve their efficiency and performance at certain stages during the operations and services. Hence, understanding these technologies is very important to realize their right use and benefits into various sectors.

Robots that resemble humans are known as androids; however, many robots aren't built on the human model. Industrial robots, for example, are often designed to perform repetitive tasks that aren't facilitated by a human-like construction. A robot can be remotely controlled by a human operator, sometimes from a great distance. A telechir is a complex robot that is remotely controlled by a human operator for a telepresence system, which gives that individual the sense of being on location in a remote, dangerous or alien environment and the ability to interact with it.

In response to Drahoslava

Deep learning uses a system of layers where input is processed and then the processed output is passed on as input to the next layer, functioning much like neurons in the human brain. Through this system of input and output processing, deep learning agents model abstract thought in data. Deep learning systems can functionally can be broken down into two major categories: retrieval and generative models.

In retrieval-based models, functionality relies on a previously collected database of responses; the AI software matches questions to answers. This is the simpler type of model and generates no new responses. Retrieval-based models work well within narrowly defined roles. In chatbot AI, they don't make grammatical errors unless those errors are in their database. However, this model is not capable of handling new questions and can tend to inconsistency when asked the same question in a semantically different way.

Deep learning uses a system of layers where input is processed and then the processed output is passed on as input to the next layer, functioning much like neurons in the human brain. Through this system of input and output processing, deep learning agents model abstract thought in data. Deep learning systems can functionally can be broken down into two major categories: retrieval and generative models.

Despite these potential risks, there are few regulations governing the use AI tools, and where laws do exist, the typically pertain to AI only indirectly. For example, federal Fair Lending regulations require financial institutions to explain credit decisions to potential customers, which limit the extent to which lenders can use deep learning algorithms, which by their nature are typically opaque.

While AI is critical for self-driving cars, the military, commerce, AI-driven SEO and gaming, it’s poised to make the most human impact in medicine and human behavior. Imagine the UN leveraging neural networks and deep learning to discover what helps some communities thrive and others fall behind. Those lessons can then be leveraged into community builders, city planners, grants and projects.

I think the greatest advantage of AI is the automation of tasks that will free up employees to focus on strategic initiatives. On the other hand, I don’t think it will be as big as predicted. There are still too many tasks that need a human touch to make them successful. We’ll see great benefit from AI in the more mundane areas, but you’ll always need the human brain for some tasks.

AI will enable us to interact with information as if we’re interacting with a knowledgeable individual. We won’t have to look at a screen to learn about anything, we can simply converse with AI. SIRI is already a reliable personal assistant when it comes to setting reminders, alarm clocks, sending texts, etc. AI will make it possible for us to do virtually anything with voice command.

The greatest benefit of AI — which is already emerging — is the elimination of repetitive tasks. From chat bots that can free up human staffers’ times to work on more complex issues, to scheduling AIs like x.ai that eliminate the need to schedule meetings, AI will ultimately help humans spend more time focusing on creative and high-mental-effort activities.

The amount of data that's typically involved, and its variety, can cause data management issues in areas including data quality, consistency and governance. Also, data silos can result from the use of different platforms and data stores in a big data architecture. In addition, integrating Hadoop, Spark and other big data tools into a cohesive architecture that meets an organization's big data analytics needs is a challenging proposition for many IT and analytics teams, which have to identify the right mix of technologies and then put the pieces together.

Big data analytics applications often include data from both internal systems and external sources, such as weather data or demographic data on consumers compiled by third-party information services providers. In addition, streaming analytics applications are becoming common in big data environments as users look to perform real-time analytics on data fed into Hadoop systems through stream processing engines, such as Spark, Flink and Storm.

In response to Maria Arlotto Duchamps

Neural networks are a class of machine learning algorithms. The neuron part of neural is the computational component and the network part is how the neurons are connected. Neural networks pass data among themselves, gathering more and more meaning as the data moves along. Because the networks are interconnected, more complex data can be processed more easily. 

Deep learning is a subset of machine learning. It refers to using multi-layered neural networks to process data in increasingly complex ways, enabling the software to train itself to perform tasks like speech and image recognition through exposure to these vast amounts of data, for continual improvement in the ability to recognize and process information. Layers of neural networks stacked on top of each for use in deep learning are called deep neural networks.

Neural networks are a class of machine learning algorithms. The neuron part of neural is the computational component and the network part is how the neurons are connected. Neural networks pass data among themselves, gathering more and more meaning as the data moves along. Because the networks are interconnected, more complex data can be processed more easily. 

Training people at entry level can be expensive for a company dealing with new technologies. Many are instead working on automation solutions involving Machine Learning and Artificial Intelligence to build insights, but this also takes well-trained staff or the outsourcing of skilled developers.

The major steps involved in a data mining process are:

• Extract, transform and load data into a data warehouse
• Store and manage data in a multidimensional databases
• Provide data access to business analysts using application software
• Present analyzed data in easily understandable forms, such as graphs

Machine learning can be defined as the practice of using algorithms to use data, learn from it and then forecast future trends for that topic. Traditional machine learning software comprised of statistical analysis and predictive analysis that are used to spot patterns and catch hidden insights based on perceived data.

Another AI area of application is Machine vision: The science of allowing computers to see. This technology captures and analyzes visual information using a camera, analog-to-digital conversion and digital signal processing. It is often compared to human eyesight, but machine vision isn't bound by biology and can be programmed to see through walls, for example. It is used in a range of applications from signature identification to medical image analysis. Computer vision, which is focused on machine-based image processing, is often conflated with machine vision.

In response to Rosemary Vandale

AI is incorporated into a variety of different types of technology. Here are two examples: Automation: What makes a system or process function automatically. For example, robotic process automation (RPA) can be programmed to perform high-volume, repeatable tasks that humans normally performed. RPA is different from IT automation in that it can adapt to changing circumstances. Machine learning: The science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics.

Rosemary,

Here are a couple more: Self-driving cars: These use a combination of computer vision, image recognition and deep learning to build automated skill at piloting a vehicle while staying in a given lane and avoiding unexpected obstructions, such as pedestrians.  Robotics: A field of engineering focused on the design and manufacturing of robots. Robots are often used to perform tasks that are difficult for humans to perform or perform consistently.

AI is incorporated into a variety of different types of technology. Here are two examples: Automation: What makes a system or process function automatically. For example, robotic process automation (RPA) can be programmed to perform high-volume, repeatable tasks that humans normally performed. RPA is different from IT automation in that it can adapt to changing circumstances. Machine learning: The science of getting a computer to act without programming. Deep learning is a subset of machine learning that, in very simple terms, can be thought of as the automation of predictive analytics.

I would not imagine a completely automated airplanes until today and this scares me. But anyway i am looking with interest the time when the full automation replace the, public transport, cooking and many more. This will be a huge step in our civilization growth and i don't want to miss it. Regards.

In response to Lucas Jessen

AI is helpful in the present and it's going to be helpful to us in the future, but what happens when you take a driver and put him/her behind a computer?

People will be forced to adapt, such proffesions are going to go extinct in the next couple of decades. One way or another we are all used to working with technology. Also new generations are ready for a change at any moment, yes maybe some will have a harder time adapting but some are already having hard time to drive. I mean it's always one or the other, but AI is making our life easier for a long time now and it's going to be making our life even easier in the future. We just have to be up to date with the new technologies.

I think that we are in the edge of a crisis with all that AI and stuff, because many people will have to retrain. This can lead to severe riots and probably, civil riot. The right approach should be choosen and people must remain calm throughout this process, but it is up to the companies to make this transition smooth.

I think that we are in the edge of a crisis with all that AI and stuff, because many people will have to retrain. This can lead to severe riots and probably, civil riot. The right approach should be choosen and people must remain calm throughout this process, but it is up to the companies to make this transition smooth.

I do not believe anyone has mentioned the application of AI in toys and games yet.  The 1990s saw some of the first attempts to mass-produce domestically aimed types of basic Artificial Intelligence for education, or leisure. This prospered greatly with the Digital Revolution, and helped introduce people, especially children, to a life of dealing with various types of Artificial Intelligence, specifically in the form of Tamagotchis and Giga Pets, iPod Touch, the Internet, and the first widely released robot, Furby. A mere year later an improved type of domestic robot was released in the form of Aibo, a robotic dog with intelligent features and autonomy.

In my opinion, one of the top benefits of living with AI will be the emergence of personalized medicine. Rather than a one-size-fits-all approach, doctors will be able to tailor treatment on an individual basis and prescribe the right treatments and procedures based on your medical history.

I believe it will be more like the science fiction movies, where we will maintain and work with the machines that do the work. However, these “jobs” will come with a level of prestige, as most people will probably live off a government sponsored socialism system. With AI and automation replacing so many jobs in the next 20 years, we will have to change social systems in order to adapt.

In response to Raakel Laukkanen

Artificial Intelligence will do wonders to help automate processes that, today, take time and manual labor but don’t contribute much to the bottom line or moving forward as a company. Automation will allow additional time and resources to be dedicated to what companies need to focus their energy on: customer experience.

Raakel,

AI does have a place in customer experience too.  Here is an article on the need and role of AI in customer experience.

Artificial Intelligence will do wonders to help automate processes that, today, take time and manual labor but don’t contribute much to the bottom line or moving forward as a company. Automation will allow additional time and resources to be dedicated to what companies need to focus their energy on: customer experience.

Reschooling of people will become essential, which means that new generation of youngsters will be very familiar with all new computer-based and internet-involved devices ( as we see it happens ). The changes are taking place right now, in front of our eyes. The thing that bothers me is that all driving-requiring professions will become obsolete, and will have to trust some AI for my life.

In response to Klas Eriksen

In my opinion we will live to see the day when Artificial Intelligence is trusted to do mundane tasks like driving vehicles.

Klas,

As AI capabilities improve, we can either treat it as a crutch that relieves us from thinking — examples include Waze and Google Maps — or as an asset that helps us use our brains more effectively and creatively.

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.

In response to Dorothea Petrescu

Hi All,

Here are a few examples of AI you are using in your lives everyday:

- smartphones

- smart cars and drones

- music and media streaming services

- videogames

- navigation and travel

- banking and finance

- security and surveillance

Anyone care to add more to the list?

Dorothea,

I have one that has always blown my mind: Googles AI powered predictions.  Using anonymized location data from smartphones Google Maps can analyze the speed of movement of traffic at any given time. And, with its acquisition of 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.

In response to Klas Eriksen

In my opinion we will live to see the day when Artificial Intelligence is trusted to do mundane tasks like driving vehicles.

Klas,

I recently saw an article in Forbes on this.  Here is what they listed as jobs that are top candidates for machine learning:

• Concierges
• Mechanical drafters
• Morticians, undertakers, and funeral directors
• Credit authorizers
• Brokerage clerks

and jobs least likely to be shredded by AI/machine learning:

• Massage therapists
• Animal scientists
• Archaeologists
• Public address system and other announcers
• Plasterers and stucco masons

In an episode of Black Mirror, an organization builds robot insects that fly around, use advanced facial recognition to find the target and kill them by climbing into their nose and burrowing into their brain.  Even with today’s technology, it is not impossible to build something like that. I won’t be surprised if the CIA is already working on it.

Aneta,

I believe the central AI claimed it was trying to save the planet from us.  They sure made a point in that movie.

Another very interesting project that takes on "living with AI" and what it could mean for us was the game "Detroit: Become Human".  It let players engage in different ways and as a result produced different outcomes.

Baldur, in the movie you mentioned, the robots all turned against us and tried to wipe out all the human race.  Do I remember correctly and what was the reason the AI acted this way?

Hi All,

Here are a few examples of AI you are using in your lives everyday:

- smartphones

- smart cars and drones

- music and media streaming services

- videogames

- navigation and travel

- banking and finance

- security and surveillance

Anyone care to add more to the list?

There was a very interesting take on living with AI that was explored in the movie "I Robot" which came out in 2004.  Here is the movie link on IMDB.  It is interesting to note, the story in the movie supposedly takes place in 2035.

AI is helpful in the present and it's going to be helpful to us in the future, but what happens when you take a driver and put him/her behind a computer?

I think the big question here is how you actually drive people to reschooling, Reskilling and so on. There's a whole new issue with economic stability, infrastrutures and services, eg: if I don't have a job and nned to go back to school I need some form of income and babystters/caretakers to look after my family.

This undermines many of the present constraints to public spending in many member states of the EU.

Moreover, how do you motivate 50-60 year olds to reschool? There issues here are much more on the social-psichology side than on the technical. 

In my opinion we will live to see the day when Artificial Intelligence is trusted to do mundane tasks like driving vehicles.

We should try and change the system so that we would own our own data and be in control of it.  I am glad you discussed privacy and data ownership in your presentation.