Category Archives: Artificial Intelligence

Access to the online databases is free to qualified researchers and medical experts to help them identify a potential treatment for the novel coronavirus.

IBM Research is making multiple free resources available to help healthcare researchers, doctors, and scientists around the world accelerate COVID-19 drug discovery. The resources can help with gathering insights, to applying the latest virus genomic information and identifying potential targets for treatments, to creating new drug molecule candidates, the company said in a statement.Though some of the resources are still in exploratory stages, IBM is giving access to qualified researchers at no charge to aid the international scientific investigation of COVID-19.The announcement follows IBM's launch of the US COVID-19 High Performance Computing Consortium, which is harnessing massive computing power in the effort to help confront the coronavirus, the company said.

Healthcare agencies and governments around the world have quickly amassed medical and other relevant data about the pandemic. And, there are already vast troves of medical research that could prove relevant to COVID-19, IBM said."Yet, as with any large volume of disparate data sources, it is difficult to efficiently aggregate and analyze that data in ways that can yield scientific insights," the company said.SEE: How tech companies are fighting COVID-19 with AI, data and ingenuity (TechRepublic)

To help researchers access structured and unstructured data quickly, IBM has offered a cloud-based AI research resource that the company said has been trained on a corpus of thousands of scientific papers contained in the COVID-19 Open Research Dataset (CORD-19), prepared by the White House and a coalition of research groups, and licensed databases from the DrugBank, Clinicaltrials.gov and GenBank.

"This tool uses our advanced AI and allows researchers to pose specific queries to the collections of papers and to extract critical COVID-19 knowledge quickly," the company said. However, access to this resource will be granted only to qualified researchers, IBM said.

The traditional drug discovery pipeline relies on a library of compounds that are screened, improved, and tested to determine safety and efficacy, IBM noted.

"In dealing with new pathogens such as SARS-CoV-2, there is the potential to enhance the compound libraries with additional novel compounds," the company said. "To help address this need, IBM Research has recently created a new, AI-generative framework which can rapidly identify novel peptides, proteins, drug candidates and materials."

This AI technology has been applied against three COVID-19 targets to identify 3,000 new small molecules as potential COVID-19 therapeutic candidates, the company said. IBM is releasing these molecules under an open license, and researchers can study them via a new interactive molecular explorer tool to understand their characteristics and relationship to COVID-19 and identify candidates that might have desirable properties to be further pursued in drug development.To streamline efforts to identify new treatments for COVID-19, IBM said it is also making the IBM Functional Genomics Platform available for free for the duration of the pandemic."Built to discover the molecular features in viral and bacterial genomes, this cloud-based repository and research tool includes genes, proteins and other molecular targets from sequenced viral and bacterial organisms in one place with connections pre-computed to help accelerate discovery of molecular targets required for drug design, test development and treatment," IBM said.

Select IBM collaborators from government agencies, academic institutions and other organizations already use this platform for bacterial genomic study, according to IBM. Now, those working on COVID-19 can request the IBM Functional Genomics Platform interface to explore the genomic features of the virus.

Clinicians and healthcare professionals on the frontlines of care will also have free access to hundreds of pieces of evidence-based, curated COVID-19 and infectious disease content from IBM Micromedex and EBSCO DynaMed, the company said.

These two decision support solutions will give users access to drug and disease information in a single and comprehensive search, according to IBM. Clinicians can also provide patients with consumer-friendly education handouts with relevant, actionable medical information, the company said.IBM's Micromedex online reference databases provide medication information that is used by more than 4,500 hospitals and health systems worldwide, according to IBM."The scientific community is working hard to make important new discoveries relevant to the treatment of COVID-19, and we're hopeful that releasing these novel tools will help accelerate this global effort," the company said."This work also outlines our long-term vision for the future of accelerated discovery, where multi-disciplinary scientists and clinicians work together to rapidly and effectively create next generation therapeutics, aided by novel AI-powered technologies."

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IBM Research releases a new set of cloud- and artificial intelligence-based COVID-19 resources - TechRepublic

Arecent study earlier this year in the journal Nature, which included researchers from Google Health London, demonstrated that artificial intelligence (AI) technology outperformed radiologists in diagnosing breast cancer on mammograms. This study is the latest to fuel ongoing speculation in the radiology industry that AI could potentially replace radiologists. However, this notion is simply sensational.

Consider the invention of autopilot. Despite its existence, passengers still rely on pilots, in conjunction with autopilot technology, to travel. Similarly, radiologists can combine their years of medical knowledge and personal patient relationships with AI technology to improve the patient and clinician experience. To examine this in greater detail, consider the scenarios in which AI is making, or can make, a positive impact.

Measuring a womans breast density is critical in assessing her risk for developing breast cancer, as women with very dense breasts are four to five times more likely to develop breast cancer than women with less dense breasts.1,2 However, as radiologists know, very dense breast tissue can create a masking effect on a traditional 2-D image, since the glandular tissue color matches that of cancer. As a result, a womans breast density classification can influence the type of breast screening exam she should get. For example, digital breast tomosynthesis (DBT) technology has proven as superior for all women, including those with dense breasts.

Categorizing density, though, can traditionally be a subjective process radiologists must manually view the breast images and make a determination, and in some cases two radiologists may disagree on a classification. This is where AI technology can make a positive impact. Through a collection of images in a database and consistent algorithms, AI technology can help unify breast density classification, especially for images teetering between a B and C BI-RADS score.

While AI technology may offer the potential to provide more consistent BI-RAD scores, the role of the radiologist is still very necessary its the radiologist who would know the patients full profile that could impact clinical care. For example, this can include other risk factors their patient may have, such as family history of breast cancer, to personal beliefs about various screening options and beyond all of which are external factors that could influence how to manage a particular patients journey of care.

In addition to helping assist with breast density classification, AI technology can also help improve workflow for radiologists which can, in turn, impact patient care. Although it is clinically proven to detect more invasive breast cancers, DBT technology produces a much larger amount of data and larger data files compared to 2-D mammography, creating workflow challenges for radiologists. However, AI technology now exists that can help reduce reading time for radiologists by identifying the critical parts of 3-D data worth preserving. The technology can then cut down on the number of images to read while maintaining image quality. The AI technology does not take over the radiologists entire role of reading the images and providing a diagnosis to patients it simply calls to their attention the higher risk images and cases that require urgent attention, allowing radiologists to prioritize cases in need of more serious and immediate scrutiny.

There are many more challenges that radiologists face today in which AI technology can potentially make an impact in the future. For example the length of time between a womans screening and the delivery of her results could use improvement, especially since that waiting period can elicit very high emotions. The important thing to realize for now, though, is that AI technology plays an important and positive role in radiology today, and the best outcomes will occur when radiologists and AI technology are not mutually exclusive but rather work in practice together.

Samir Parikh is the global vice president of research and development for Hologic. In this role, he is responsible for leading and driving innovative advanced solutions across the continuum of care to drive sustainable growth of the breast and skeletal health division.

References:

1.Boyd NF, Guo H, Martin LJ, et al. Mammographic density and the risk and detection of breast cancer. N Engl J Med. 356(3):227-36, 2007.

2. Yaghjyan L, Colditz GA, Collins LC, et al. Mammographic breast density and subsequent risk of breast cancer in postmenopausal women according to tumor characteristics. J Natl Cancer Inst. 103(15):1179-89, 2011.

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Leveraging Artificial Intelligence to Enhance the Radiologist and Patient Experience - Imaging Technology News

The Central Board of Secondary Education (CBSE) will introduce Design Thinking, Physical Activity Trainer and Artificial Intelligence as new subjects for class 11 from the 2020-21 academic year, officials have revealed.

To make the new generation more creative, innovative and physically fit, and to keep pace with global developments and requirements in the workplace, the board is introducing the three new subjects, said Biswajit Saha, Director Training and Skill Education, CBSE.

"While thinking is a skill that all humans possess, the 21st century's requirement is of critical thinking and problem-solving. Design Thinking is a systematic process of thinking that opens up the horizons of creativity and enables even the most conditioned thinkers to bring about new and innovative solutions to the problems at hand," he said.

According to Saha, the course on Physical Activity Trainer will not only help in developing skills of a trainer but also a life skill.

"Artificial Intelligence is also a simulation by machines of the unlimited thinking capacity of humans. Physical Activity is a must if the body and mind are to be kept healthy.

"With this view in mind, the course on Physical Activity Trainer has been prepared. It will not only help in developing the skill of a trainer, but will also become a life skill as it will imbibe the idea of keeping fit for life," he added.

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Artificial intelligence to be added to class 11 curriculum in India - Khaleej Times

How does Artificial Intelligence work?

Artificial Intelligence is a complex field with many components and methodologies used to achieve the final result an intelligent machine. AI was developed by studying the way the human brain thinks, learns and decides, then applying those biological mechanisms to computers.

As opposed to classical computing, where coders provide the exact inputs, outputs, and logic, artificial intelligence is based on providing a machine the inputs and a desired outcome, letting the machine develop its own path to achieve its set goal. This frequently allows computers to better optimize a situation than humans, such as optimizing supply chain logistics and streamlining financial processes.

There are four types of AI that differ in their complexity of abilities:

Artificial intelligence is used in virtually all businesses; in fact, you likely interact with it in some capacity on a daily basis. Chatbots, smart cars, IoT devices, healthcare, banking, and logistics all use artificial intelligence to provide a superior experience.

One AI that is quickly finding its way into most consumers homes is the voice assistant, such as Apples Siri, Amazons Alexa, Googles Assistant, and Microsofts Cortana. Once simply considered part of a smart speaker, AI-equipped voice assistants are now powerful tools deeply integrated across entire ecosystems of channels and devices to provide an almost human-like virtual assistant experience.

Dont worry we are still far from a Skynet-like scenario. AI is as safe as the technology it is built upon. But keep in mind that any device that uses AI is likely connected to the internet, and given that internet connected device security isnt perfect and we continue to see large company data breaches, there could be AI vulnerabilities if the devices are not properly secured.

Startups and legacy players alike are investing in AI technology. Some of the leaders include household names like:

As well as newcomers such as:

APEX Technologies was also ranked as the top artificial intelligence company in China last year.

You can read our full list of most innovative AI startups to learn more.

Artificial intelligence can help reduce human error, create more precise analytics, and turn data collecting devices into powerful diagnostic tools. One example of this is wearable devices such as smartwatches and fitness trackers, which put data in the hands of consumers to empower them to play a more active role managing their health.

Learn more about how tech startups are using AI to transform industries like digital health and transportation.

Then-Dartmouth College professor John McCarthy coined the term, artificial intelligence, and is widely known as the father of AI. in the summer of 1956, McCarthy, along with nine other scientists and mathematicians from Harvard, Bell Labs, and IBM, developed the concept of programming machines to use language and solve problems while improving over time.

McCarthy went on to teach at Stanford for nearly 40 years and received the Turing Award in 1971 for his work in AI. He passed away in 2011.

Open application programming interfaces (APIs) are publicly available governing requirements on how an application can communicate and interact. Open APIs provide developers access to proprietary software or web services so they can integrate them into their own programs. For example, you can create your own chatbot using this framework.

As you could imagine, artificial intelligence technology is evolving daily and Business Insider Intelligence keeping its finger on the pulse of how artificial intelligence will shape the future of a variety of industries, such as the Internet of Things (IoT), transportation and logistics, digital health, and multiple branches of fintech including insurtech and life insurance.

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Artificial Intelligence News: Latest Advancements in AI ...

You have probably heard that artificial intelligence could be used to do lots of impressive tasks and jobs. AI can help designers and artists make quick tweaks to visuals. AI can also help researchers identify fake images or connect touch and sense. AI is being used to program websites and apps by combining symbolic reasoning and deep learning. Basically, artificial intelligence goes beyond deep learning. Here are five reasons why AI is important to you.

It is no news that AI will replace repetitive jobs. It literally means that these kinds of jobs will be automated, like what robots are currently doing in a myriad of factories. Robots are rendering the humans that are supposed to do those tasks practically jobless.

And it goes further than that many white collar tasks in the fields of law, hospitality, marketing, healthcare, accounting, and others are adversely affected. The situation seems scary because scientists are just scratching the surface as extensive research and development of AI. AI is advancing rapidly (and it is more accessible to everybody).

Some believe that AI can create even more new jobs than ever before. According to this school of thought, AI will be the most significant job engine the world has ever seen. Artificial intelligence will eliminate low-skilled jobs and effectively create massive high-skilled job opportunities that will span all sectors of the economy.

For example, if AI becomes fully adapt to language translation, it will create a considerable demand for high-skilled human translators. If the costs of essential translations drop to nearly zero, this will encourage MORE companies that need this particular service to expand their business operations abroad.

To those who speak different languages than the community in which they reside, this help will inevitably create more work for high-skilled translators, boost more economic activities. As a result of this, and more people will be employed in these companies due to the increased workload.

Boosting international trade it one of the most significant benefits of our global times. So yes, AI will eliminate some jobs, but it will create many, many more.

AI can be used extensively in the healthcare industry. It is applicable in automated operations, predictive diagnostics, preventive interventions, precision surgery, and a host of other clinical operations. Some individuals predict that AI will completelyreshape the healthcare landscape for the better.

And here are some of the applications of artificial intelligence in healthcare:

AI is also used in the agriculture industry extensively. Robots can be used to plant seeds, fertilized crops and administer pesticides, among a lot of other uses. Farmers can use a drone to monitor the cultivation of crops and also collect data for analysis.

The value-add data will be used to increase the final output. How? The data collected is analyzed by AI on such variables as crop health and soil conditions, boosting final production, and it can also be used in harvesting, especially for crops that are difficult to gather.

AI is changing the workplace, and there are plenty of reasons to be optimistic. It is used to do lots of tedious and lengthy tasks, especially the low-skilled types of jobs that are labor-intensive. It means that employees will be retasked away from boring jobs and bring significant and positive change in the workplace.

For instance, artificial intelligence is used in the automotive industry to do repetitive tasks such as performing a routine operation in the assembly line, for example. Allowing a robot to care for well, robotic-tasks, has created a shift in the workforce.

Auto accidents are one of the most popular types of accidents that happen in America. It kills thousands of people annually. A whopping 95 percent of these accidents are caused byhuman error, meaning accidents are avoidable.

The number of accident cases will reduce as artificial intelligence is being introduced into the industry by the use of self-driving cars. On-going research in the auto industry is looking at ways AI can be used to improve traffic conditions.

Smart systems are currently in place in many cities that are used to analyze traffic lights at the intersections. Avoiding congestion leads to safer movements of vehicles, bicycles, and pedestrians.

Conclusion

Artificial intelligence is very useful in all industries as more research is being done to advance it. The advancements in this AI tech will be most useful if it is understood and trusted. An important part of it is that artificial intelligence and related technologies such as drones, robots, and autonomous vehicles can create around tens of millions of jobs over the next decade.

Having more jobs created not less will be great news for everyone. More jobs will help boost the GDP of the economy. Advancement in AI and its impressive computational power has already led to the concept of supercomputers and beyond.

Elena Randall is a Content Creator Who works for Top Software Companies, provides a top 10 list of top software development companies within the world. She is passionate about reading and writing.

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5 Reasons Why Artificial Intelligence Is Important To You

Intelligence is described as the ability to adapt to new environments and situations and being able to understand consequences and effects that your actions cause. This is something that all living creatures have in some way or another. Animals adapt to their environments and react to interference, plants do the same. Human intelligence, however, is in an entirely different ball park. With the uprise of technology and advancements constantly being made, it has now come time to question the use of artificial intelligence. Artificial intelligence, or AI, means giving non living things, such as computers and robots, the ability to think for themselves to an extent. What would this mean for the future? Would the economy, society, and the world as we know it change for the better or worse?

No BreaksOne of the biggest benefits to using machines with some level of artificial intelligence is that they could be utilized to do necessary jobs more efficiently. Machines do not need to take breaks in the way that humans do. They do not need to sleep, eat, or use the restroom. This would allow businesses to produce goods twenty four hours a day, 365 days a year.

Inhumane CircumstancesArtificial intelligence has allowed many avenues in research and exploration to develop and advance that would not have if it did not exist. This is especially true with space exploration. The satellites and rovers that are being sent in to space all of the time can stay there forever, and continue to reach further and further out into our solar system, giving us a much better understanding of what lays in the beyond.

No Emotional BarriersIntelligent machines do not have emotions. This is greatly beneficial because nothing interferes with their ability to perform the task they were designed to do. This is completely untrue with humans, many people find it difficult to work under very stressful conditions or during times of trauma.

Cost EfficientMachines do not need to receive a paycheck every month. While they are quite costly to maintain and power, this cost is greatly less than what an entire company full of human employees would have to be paid. The costs are also minimized and controlled.

Job LossWith the introduction of machines that can complete humans jobs quicker, more accurate, and cheaper, the rate of jobs lost is climbing. Ever since the introduction of factory machines people have been losing jobs to technologies.

Personal ConnectionsAnother large concern to think about when it comes to artificial intelligence is their lack of compassion and sympathy. If these robots are introduced into fields such as healthcare, how can we ensure the patients and customers comfort? Sure, they can be programmed to care, but it is not genuine.

Loss of InformationWe have seen it time and time again, and probably even experienced it once or twice, information being lost due to machine damages. The majority of our documents, videos and images are all stored on computers, phones, and other forms of technology. Many things can cause this information to be lost in an instant, and also non retrievable. This could pose very large problems if artificial intelligence is implemented in areas such as banks or healthcare.

Evolved?It may seem like a science fiction movie, but what would really happen if these artificially intelligent machines began to think for themselves, literally? It could pose major security risks, but it sure does make a great story line.

Technology and advancements in this field are happening, and they are happening very fast. It is something that we cannot and will not stop, so it is best to embrace the changing world we live in and take advantage of all of the incredible things that we have access to.

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Pros and Cons of Artificial Intelligence - HRF

Artificial intelligence is also being used to analyse vast amounts of molecular information looking for potential new drug candidates a process that would take humans too long to be worth doing. Indeed, machine learning could soon be indispensable to healthcare.

Artificial intelligence can also help us manage highly complex systems such as global shipping networks. For example, the system at the heart of the Port Botany container terminal in Sydney manages the movement of thousands of shipping containers in and out of the port, controlling a fleet of automated, driverless straddle-carriers in a completely human-free zone. Similarly, in the mining industry, optimisation engines are increasingly being used to plan and coordinate the movement of a resource, such as iron ore, from initial transport on huge driverless mine trucks, to the freight trains that take the ore to port.

AIs are at work wherever you look, in industries from finance to transportation, monitoring the share market for suspicious trading activity or assisting with ground and air traffic control. They even help to keep spam out of your inbox. And this is just the beginning for artificial intelligence. As the technology advances, so too does the number of applications.

SO WHATS THE PROBLEM?

Rather than worrying about a future AI takeover, the real risk is that we can put too much trust in the smart systems we are building. Recall that machine learning works by training software to spot patterns in data. Once trained, it is then put to work analysing fresh, unseen data. But when the computer spits out an answer, we are typically unable to see how it got there.

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The real risks of artificial intelligence - BBC Future

Artificial intelligence, or AI, is a computer system which learns from the experiences it encounters. It can adjust on its own to new inputs, allowing it to perform tasks in a way that is similar to what a human would do. How we have defined AI over the years has changed, as have the tasks weve had these machines complete.

As a term, artificial intelligence was defined in 1956. With increasing levels of data being processed, improved storage capabilities, and the development of advanced algorithms, AI can now mimic human reasoning. AI personal assistants, like Siri or Alexa, have been around for military purposes since 2003.

With these artificial intelligence pros and cons, it is important to think of this technology as a decision support system. It is not the type of AI from science-fiction stories which attempts to rule the world by dominating the human race.

1. Artificial intelligence completes routine tasks with ease.Many of the tasks that we complete every day are repetitive. That repetition helps us to get into a routine and positive work flow. It also takes up a lot of our time. With AI, the repetitive tasks can be automated, finely tuning the equipment to work for extended time periods to complete the work. That allows human workers to focus on the more creative elements of their job responsibilities.

2. Artificial intelligence can work indefinitely.Human workers are typically good for 8-10 hours of production every day. Artificial intelligence can continue operating for an indefinite time period. As long as there is a power resource available to it, and the equipment is properly cared for, AI machines do not experience the same dips in productivity that human workers experience when they get tired at the end of the day.

3. Artificial intelligence makes fewer errors.AI is important within certain fields and industries where accuracy or precision is the top priority. When there are no margins for error, these machines are able to breakdown complicated math constructs into practical actions faster, and with more accuracy, when compared to human workers.

4. Artificial intelligence helps us to explore.There are many places in our universe where it would be unsafe, if not impossible, for humans to see. AI makes it possible for us to learn more about these places, which furthers our species knowledge database. We can explore the deepest parts of the ocean because of AI. We can journey to inhospitable planets because of AI. We can even find new resources to consume because of this technology.

5. Artificial intelligence can be used by anyone.There are multiple ways that the average person can embrace the benefits of AI every day. With smart homes powered by AI, thermostat and energy regulation helps to cut the monthly utility bill. Augmented reality allows consumers to picture items in their own home without purchasing them first. When it is correctly applied, our perception of reality is enhanced, which creates a positive personal experience.

6. Artificial intelligence makes us become more productive.AI creates a new standard for productivity. It will also make each one of us more productive as well. If you are texting someone or using word processing software to write a report and a misspelled word is automatically corrected, then youve just experienced a time benefit because of AI. An artificial intelligence can sift through petabytes of information, which is something the human brain is just not designed to do.

7. Artificial intelligence could make us healthier.Every industry benefits from the presence and use of AI. We can use AI to establish healthier eating habits or to get more exercise. It can be used to diagnose certain diseases or recommends a treatment plan for something already diagnosed. In the future, AI might even assist physicians who are conducting a surgical procedure.

8. Artificial intelligence extends the human experience.With an AI helping each of us, we have the power to do more, be more, and explore more than ever before. In some ways, this evolutionary process could be our destiny. Some believe that computers and humanity are not separate, but instead a single, cognitive unit that already works together for the betterment of all. Through AI, people who are blind can now see. Those who are deaf can now hear. We become better because we have a greater capacity to do thins.

1. Artificial intelligence comes with a steep price tag.A new artificial intelligence is costly to build. Although the price is coming down, individual developments can still be as high as $300,000 for a basic AI. For small businesses operating on tight margins or low initial capital, it may be difficult to find the cash necessary to take advantage of the benefits which AI can bring. For larger companies, the cost of AI may be much higher, depending upon the scope of the project.

2. Artificial intelligence will reduce employment opportunities.There will be jobs gained because of AI. There will also be jobs lost because of it. Any job which features repetitive tasks as part of its duties is at-risk of being replaced by an artificial intelligence in the future. In 2017, Gartner predicted that 500,000 net jobs would be created because of AI. On the other end of the spectrum, up to 900,000 jobs could be lost because of it. Those figures are for jobs only within the United States.

3. Artificial intelligence will be tasked with its own decisions.One of the greatest threats we face with AI is its decision-making mechanism. An AI is only as intelligent and insightful as the individuals responsible for its initial programming. That means there could be a certain bias found within is mechanisms when it is time to make an important decision. In 2014, an active shooter situation caused people to call Uber to escape the area. Instead of recognizing the dangerous situation, the algorithm Uber used saw a spike in demand, so it decided to increase prices.

4. Artificial intelligence lacks creativity.We can program robots to perform creative tasks. Where we stall out in the evolution of AI is creating an intelligence which can be originally creative on its own. Our current AI matches the creativity of its creator. Because there is a lack of creativity, there tends to be a lack of empathy as well. That means the decision of an AI is based on what the best possible analytical solution happens to be, which may not always be the correct decision to make.

5. Artificial intelligence can lack improvement.An artificial intelligence may be able to change how it reacts in certain situations, much like a child stops touching a hot stove after being burned by it. What it does not do is alter its perceptions, responses, or reactions when there is a changing environment. There is an inability to distinguish specific bits of information observed beyond the data generated by that direct observation.

6. Artificial intelligence can be inaccurate.Machine translations have become an important tool in our quest to communicate with one another universally. The only problem with these translations is that they must be reviewed by humans because the words, not the intent of the words, is what machines translate. Without a review by a trained human translator, the information received from a machine translation may be inaccurate or insensitive, creating more problems instead of fewer with our overall communication.

7. Artificial intelligence changes the power structure of societies.Because AI offers the potential to change industries and the way we live in numerous ways, societies experience a power shift when it becomes the dominant force. Those who can create or control this technology are the ones who will be able to steer society toward their personal vision of how people should be. It also removes the humanity out of certain decisions, like the idea of having autonomous AI responsible for warfare without humans actually initiating the act of violence.

8. Artificial intelligence treats humanity as a commodity.When we look at the possible outcomes of AI on todays world, the debate is often about how many people benefit compared to how many people will not. The danger here is that people are treated as a commodity. Businesses are already doing this, looking at the commodity of automation through AI as a better investment than the commodity of human workers. If we begin to perceive ourselves as a commodity only, then AI will too, and the outcome of that decision could be unpredictable.

These artificial intelligence pros and cons show us that our world can benefit from its presence in a variety of ways. There are also many potential dangers which come with this technology. Jobs may be created, but jobs will be lost. Lives could be saved, but lives could also be lost. That is why the technologies behind AI must be made available to everyone. If only a few hold the power of AI, then the world could become a very different place in a short period of time.

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16 Artificial Intelligence Pros and Cons Vittana.org

Alan Crameri, CTO, Barrachd explains that the rise of artificial intelligence will lead to the fourth industrial revolution

'AI is a journey. And the journey to AI starts with 'the basics' of identifying and understanding the data. Where does it reside? How can we access it? We need strong information architecture as the first step on our AI ladder'.

Artificial Intelligence (AI) has been described as the fourth industrial revolution. It will transform all of our jobs and lives over the next 10 years. However, it is not a new concept. AIs roots are in the expert systems of the 70s and 80s, computers that were programmed with a humans expert knowledge in order to allow decision-making based on the available facts.

Whats different today, and is enabling this revolution, is the evolution of machine learning systems. No longer are machines just capturing explicit knowledge (where a human can explain a series of fairly logical steps). They are now developing a tacit knowledge the intuitive, know-how embedded in the human mind. The kind of knowledge thats hard to describe, let alone transfer.

Machine learning is already all around us, unlocking our phones with a glance or a touch, suggesting music we like to listen to, and teaching cars to drive themselves.

>Read more onArtificial Intelligence what CTOs and co need to know

Underpinning all this is the explosion of data. Data is growing faster than ever before. By the year 2020, its estimated that every human being on the planet will be creating 1.7 megabytes of new information every second! There will be 50 billion smart connected devices in the world, all developed to collect, analyse and share data. This data is vital to AI. Machine learning models need data Just as we humans learn our tacit knowledge through our experiences, by attempting a task again and again to gradually improve, ML models need to be trained.

AI is a journey. And the journey to AI starts with the basics of identifying and understanding the data. Where does it reside? How can we access it? We need strong information architecture as the first step on our AI ladder.

Of course, some data may be difficult it might be unstructured, it may need refinement, it could be in disparate locations and from different sources. So, the next step is to fuse together this data in order to allow analytics tools to find better insight.

The next step in the journey is identifying and understanding the patterns and trends in our data with smart analytics techniques.

>Read more onA guide to artificial intelligence in enterprise: Is it right for your business?

Only once these steps of the journey have been completed can we truly progress to AI and machine learning, to gain further insight into the past and future performance of our organisations, and to help us solve business problems more efficiently.

But once that journey is complete the architecture, the data fusion, the analytics solutions the limits of possibility are only contained by the availability of data. So lets look at some examples where were already using these techniques.

Lets take an example that is applicable to most organisations the management of people. Businesses can fuse employee and payroll data, absence records, training records, performance ratings and more to give a complete picture of an employees interaction with the organisation. Managers can instantly visualise how people are performing, and which areas to focus on for improvement. The next stage is to use AI models to predict those employees who might need some extra support or intervention high-performers at risk of leaving, or people showing early signs of declining performance.

But what about when you focus instead on the customer? Satisfaction, retention, and interaction increasingly businesses look to social media to track the sentiment and engagement of their relationships with customers and consumers. Yet finding meaningful patterns and insights amongst a continual flow of diverse data can be difficult.

Social media analytics solutions can be used to analyse how customers and consumers view and react to the companies and brands theyre interacting with through social media.

>Read more onArtificial intelligence: Transforming the insurance industry

The data is external to the organisations concerned but is interpreted to create an information architecture behind the scenes. The next stop on the AI journey enables powerful analysis of trends and consumer behaviour over time, allowing organisations to track and forecast customer engagement in real-time.

Social media data isnt the only source of real time engagement. Customer data is an increasingly rich vein that can be tapped into. Disney is already collecting location data from wristbands at their attractions, predicting and managing queue lengths (suggesting other rides with shorted queues, or offering food/drink vouchers in busy times to reduced demand). Infrared cameras are even watching people in movie theatres and monitoring eye movements and facial expressions to determine engagement and sentiment.

The ability to analyse increasingly creative and diverse data sources to unearth new insights is growing, but the ability to bring together these new, disparate data sources is key to realising their value.

There are huge opportunities around the sharing and fusion of data, in particular between different agencies (local government, health, police). But this comes with significant challenges around privacy, data protection and a growing public concern.

The next step is to predict the future when and where crime is likely to happen, or the risk or vulnerability of individuals, allowing the police to direct limited resources as efficiently as possible. Machine learning algorithms can be employed in a variety of ways to automate facial recognition, to pinpoint crime hotspots, and to identify which people are more likely to reoffend.

>Read more onArtificial intelligence: Data will be the differentiator in the marketplace

AI models are good at learning to recognise patterns. And these patterns arent just found in images, but in sound too. Models already exist that can listen to the sounds within a city, and detect the sound of a gunshot a large proportion of which go unreported. Now lamppost manufacturers are building smart street lights, which monitor light, sound, weather and other environmental variants. By introducing new AI models, could we allow them to detect gunshots at scale, helping police to respond quickly and instantly when a crime is underway?

However, there is one underlying factor that occurs across every innovative solution now, and in the future. Data quality. IBM has just launched an AI tool deigned to monitor artificial intelligence deployments, and assess accuracy, fairness and bias in the decisions that they make. In short, AI models monitoring other AI models.

Lets just hope that the data foundation that these are built on is correct at the end of the day, if the underlying data is flawed, then so will be the AI model, and so will be the AI monitoring the AI! And thats why the journey to advanced analytics, AI and machine learning is so important. Building a strong information architecture, investing in intelligent data fusion and creating a solid analytics foundation is vital to the success of future endeavours in data.

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Artificial Intelligence: The fourth industrial revolution

by Rockwell Anyoha

In the first half of the 20th century, science fiction familiarized the world with the concept of artificially intelligent robots. It began with the heartless Tin man from the Wizard of Oz and continued with the humanoid robot that impersonated Maria in Metropolis. By the 1950s, we had a generation of scientists, mathematicians, and philosophers with the concept of artificial intelligence (or AI) culturally assimilated in their minds. One such person was Alan Turing, a young British polymath who explored the mathematical possibility of artificial intelligence. Turing suggested that humans use available information as well as reason in order to solve problems and make decisions, so why cant machines do the same thing? This was the logical framework of his 1950 paper, Computing Machinery and Intelligence in which he discussed how to build intelligent machines and how to test their intelligence.

Unfortunately, talk is cheap. What stopped Turing from getting to work right then and there? First, computers needed to fundamentally change. Before 1949 computers lacked a key prerequisite for intelligence: they couldnt store commands, only execute them. In other words, computers could be told what to do but couldnt remember what they did. Second, computing was extremely expensive. In the early 1950s, the cost of leasing a computer ran up to $200,000 a month. Only prestigious universities and big technology companies could afford to dillydally in these uncharted waters. A proof of concept as well as advocacy from high profile people were needed to persuade funding sources that machine intelligence was worth pursuing.

Five years later, the proof of concept was initialized through Allen Newell, Cliff Shaw, and Herbert Simons, Logic Theorist. The Logic Theorist was a program designed to mimic the problem solving skills of a human and was funded by Research and Development (RAND) Corporation. Its considered by many to be the first artificial intelligence program and was presented at the Dartmouth Summer Research Project on Artificial Intelligence (DSRPAI) hosted by John McCarthy and Marvin Minsky in 1956. In this historic conference, McCarthy, imagining a great collaborative effort, brought together top researchers from various fields for an open ended discussion on artificial intelligence, the term which he coined at the very event. Sadly, the conference fell short of McCarthys expectations; people came and went as they pleased, and there was failure to agree on standard methods for the field. Despite this, everyone whole-heartedly aligned with the sentiment that AI was achievable. The significance of this event cannot be undermined as it catalyzed the next twenty years of AI research.

From 1957 to 1974, AI flourished. Computers could store more information and became faster, cheaper, and more accessible. Machine learning algorithms also improved and people got better at knowing which algorithm to apply to their problem. Early demonstrations such as Newell and Simons General Problem Solver and Joseph Weizenbaums ELIZA showed promise toward the goals of problem solving and the interpretation of spoken language respectively. These successes, as well as the advocacy of leading researchers (namely the attendees of the DSRPAI) convinced government agencies such as the Defense Advanced Research Projects Agency(DARPA) to fund AI research at several institutions. The government was particularly interested in a machine that could transcribe and translate spoken language as well as high throughput data processing. Optimism was high and expectations were even higher. In 1970 Marvin Minsky told Life Magazine, from three to eight years we will have a machine with the general intelligence of an average human being. However, while the basic proof of principle was there, there was still a long way to go before the end goals of natural language processing, abstract thinking, and self-recognition could be achieved.

Breaching the initial fog of AI revealed a mountain of obstacles. The biggest was the lack of computational power to do anything substantial: computers simply couldnt store enough information or process it fast enough. In order to communicate, for example, one needs to know the meanings of many words and understand them in many combinations. Hans Moravec, a doctoral student of McCarthy at the time, stated that computers were still millions of times too weak to exhibit intelligence. As patience dwindled so did the funding, and research came to a slow roll for ten years.

In the 1980s, AI was reignited by two sources: an expansion of the algorithmic toolkit, and a boost of funds. John Hopfield and David Rumelhart popularized deep learning techniques which allowed computers to learn using experience. On the other hand Edward Feigenbaum introduced expert systems which mimicked the decision making process of a human expert. The program would ask an expert in a field how to respond in a given situation, and once this was learned for virtually every situation, non-experts could receive advice from that program. Expert systems were widely used in industries. The Japanese government heavily funded expert systems and other AI related endeavors as part of their Fifth Generation Computer Project (FGCP). From 1982-1990, they invested $400 million dollars with the goals of revolutionizing computer processing, implementing logic programming, and improving artificial intelligence. Unfortunately, most of the ambitious goals were not met. However, it could be argued that the indirect effects of the FGCP inspired a talented young generation of engineers and scientists. Regardless, funding of the FGCP ceased, and AI fell out of the limelight.

Ironically, in the absence of government funding and public hype, AI thrived. During the 1990s and 2000s, many of the landmark goals of artificial intelligence had been achieved. In 1997, reigning world chess champion and grand master Gary Kasparov was defeated by IBMs Deep Blue, a chess playing computer program. This highly publicized match was the first time a reigning world chess champion loss to a computer and served as a huge step towards an artificially intelligent decision making program. In the same year, speech recognition software, developed by Dragon Systems, was implemented on Windows. This was another great step forward but in the direction of the spoken language interpretation endeavor. It seemed that there wasnt a problem machines couldnt handle. Even human emotion was fair game as evidenced by Kismet, a robot developed by Cynthia Breazeal that could recognize and display emotions.

We havent gotten any smarter about how we are coding artificial intelligence, so what changed? It turns out, the fundamental limit of computer storage that was holding us back 30 years ago was no longer a problem. , which estimates that the memory and speed of computers doubles every year, had finally caught up and in many cases, surpassed our needs. This is precisely how Deep Blue was able to defeat Gary Kasparov in 1997, and how Googles Alpha Go was able to defeat Chinese Go champion, Ke Jie, only a few months ago. It offers a bit of an explanation to the roller coaster of AI research; we saturate the capabilities of AI to the level of our current computational power (computer storage and processing speed), and then wait for Moores Law to catch up again.

We now live in the age of big data, an age in which we have the capacity to collect huge sums of information too cumbersome for a person to process. The application of artificial intelligence in this regard has already been quite fruitful in several industries such as technology, banking, marketing, and entertainment. Weve seen that even if algorithms dont improve much, big data and massive computing simply allow artificial intelligence to learn through brute force. There may be evidence that Moores law is slowing down a tad, but the increase in data certainly hasnt lost any momentum. Breakthroughs in computer science, mathematics, or neuroscience all serve as potential outs through the ceiling of Moores Law.

So what is in store for the future? In the immediate future, AI language is looking like the next big thing. In fact, its already underway. I cant remember the last time I called a company and directly spoke with a human. These days, machines are even calling me! One could imagine interacting with an expert system in a fluid conversation, or having a conversation in two different languages being translated in real time. We can also expect to see driverless cars on the road in the next twenty years (and that is conservative). In the long term, the goal is general intelligence, that is a machine that surpasses human cognitive abilities in all tasks. This is along the lines of the sentient robot we are used to seeing in movies. To me, it seems inconceivable that this would be accomplished in the next 50 years. Even if the capability is there, the ethical questions would serve as a strong barrier against fruition. When that time comes (but better even before the time comes), we will need to have a serious conversation about machine policy and ethics (ironically both fundamentally human subjects), but for now, well allow AI to steadily improve and run amok in society.

Rockwell Anyoha is a graduate student in the department of molecular biology with a background in physics and genetics. His current project employs the use of machine learning to model animal behavior. In his free time, Rockwell enjoys playing soccer and debating mundane topics.

This article is part of a Special Edition on Artificial Intelligence.

Brief Timeline of AI

https://www.livescience.com/47544-history-of-a-i-artificial-intelligence-infographic.html

Complete Historical Overview

http://courses.cs.washington.edu/courses/csep590/06au/projects/history-ai.pdf

Dartmouth Summer Research Project on Artificial Intelligence

https://www.aaai.org/ojs/index.php/aimagazine/article/view/1904/1802

Future of AI

https://www.technologyreview.com/s/602830/the-future-of-artificial-intelligence-and-cybernetics/

Discussion on Future Ethical Challenges Facing AI

http://www.bbc.com/future/story/20170307-the-ethical-challenge-facing-artificial-intelligence

Detailed Review of Ethics of AI

https://intelligence.org/files/EthicsofAI.pdf

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The History of Artificial Intelligence - Science in the News