Monthly Archives: July 2017

Scientists are using artificial intelligence (AI) to help predict which embryos will result inIVFsuccess.

In a new study, AI was found to be more accurate than embryologists at pinpointing which embryos had the potential to result in the birth of a healthy baby.

Experts from Sao Paulo State University in Brazil have teamed up with Boston Place Clinic in London to develop the technology in collaboration with Dr Cristina Hickman, scientific adviser to the British Fertility Society.

They believe the inexpensive technique has the potential to transform care for patients and help women achieve pregnancy sooner.

During the process, AI was trained in what a good embryo looks like from a series of images.

AI is able to recognise and quantify 24 image characteristics of embryos that are invisible to the human eye.

These include the size of the embryo, texture of the image and biological characteristics such as the number and homogeneity of cells.

During the study, which used cattle embryos, 48 images were evaluated three times each by embryologists and by the AI system.

The embryologists could not agree on their findings across the three images, but AI led to complete agreement.

Stuart Lavery, director of the Boston Place Clinic, said the technology would not replace examining chromosomes in detail, which is thought to be a key factor in determining which embryos are normal or abnormal.

He said: Looking at chromosomes does work, but it is expensive and it is invasive to the embryo.

What we are looking for here is something that can be universal.

Instead of a human looking at thousands of images, actually a piece of software looks at them and is capable of learning all the time.

As we get data about which embryos produce a baby, that data will be fed back into the computer and the computer will learn.

What we have found is that the technique is much more consistent than an embryologist, it is more reliable.

It can also look for things that the human eye can't see.

We don't think it will replace genetic screening we think it will be a complimentary to this type of screening.

Analysis of the embryo won't improve the chances of that particular embryo, but it will help us pick the best one.

We won't waste time on treatments that won't work, so the patient should get pregnant quicker.

He said work was under way to look back at images from parents who had genetic screening and became pregnant. Applying AI to those images will help the computer learn, he said.

Mr Lavery added: This is an innovative and exciting project combining state of the art embryology with new advances in computer modelling, all with the aim of selecting the best possible embryo for transfer to give all our patients the best possible chance of having a baby.

Although further work is needed to optimise the technique, we hope that a system will be available shortly for use in a clinical setting.

Continued here:

Artificial intelligence better than scientists at choosing successful IVF embryos - The Independent

It is 60 years since Artificial Intelligence (AI) was first recognised as an academic discipline, but it is only in the 21st Century that AI has caught both businesses interest and the publics imagination.

Smartphones, smart hubs, and speech recognition have brought AI simulations to homes and pockets, autonomous vehicles are on our roads, and enterprise apps promise to reveal hidden truths about data of every size, and the people or behaviors it describes.

But AI doesnt just refer to a machine that is intelligent in terms of its operation, but also in terms of its social consequences. Thats the alarm bell sounding in the most thought-provoking report on AI to appear recently Artificial Intelligence and Robotics, a 56-page white paper published by UK-RAS, the umbrella body for British robotics research.

The upside of AI is easily expressed:

Current state-of-the-art AI allows for the automation of various processes, and new applications are emerging with the potential to change the entire workings of the business world. As a result, there is huge potential for economic growth.

One-third of the report explores the history of AIs development which is recommended reading but the authors get to the nitty gritty of its application right away:

A clear strategy is required to consider the associated ethical and legal challenges to ensure that society as a whole will benefit from AI, and its potential negative impact is mitigated from early on.

Neither the unrealistic enthusiasm, nor the unjustified fears of AI, should hinder its progress. [Instead] they should be used to motivate the development of a systemic framework on which the future of AI will flourish.

And AI is certainly flourishing, it adds:

The revenues of the AI market worldwide, were around $260 billion in 2016 and this is estimated to exceed $3,060 billion by 2024. This has had a direct effect on robotic applications, including exoskeletons, rehabilitation, surgical robots, and personal care-bots. [] The economic impact of the next 10 years is estimated to be between $1.49 and $2.95 trillion.

For vendors and their customers, AI is the new must-have differentiator. Yet in the context of what the report calls unrealistic enthusiasm about it, the need to understand AIs social impact is both urgent and overwhelming.

As AI, big data, and the related fields of machine learning, deep learning, and computer vision/object recognition rise, buyers and sellers are rushing to include AI in everything, from enterprise CRM to national surveillance programmes. An example of the latter is the FBIs scheme to record and analyse citizens tattoos in order to establish if people who have certain designs inked on their skin are likely to commit crimes*.

Such projects should come with the label Because we can.

In such a febrile environment, the risk is that the twin problems of confirmation bias in research and human prejudice in society become an automated pandemic: systems that are designed to tell people exactly what they want to hear; or software that perpetuates profound social problems.

This is neither alarmist, nor an overstatement. The white paper notes:

In an article published by Science magazine, researchers saw how machine learning technology reproduces human bias, for better or for worse. [AI systems] reflect the links that humans have made themselves.

These are real-world problems. Take the facial recognition system developed at MIT recently that was unable to identify an African American woman, because it was created within a closed group of white males male insularity is a big problem in IT. When Media Lab chief Joichi Ito shared this story at Davos earlier this year, he described his own students as oddballs.*

The white paper adds its own example of human/societal bias entering AI systems:

When an AI program became a juror in a beauty contest in September 2016, it eliminated most black candidates as the data on which it had been trained to identify beauty did not contain enough black skinned people.

Now apply this model in, say, automated law enforcement

The point is that human bias infects AI systems at both linguistic and cultural levels. Code replicates belief systems including their flaws, prejudices, and oversights while coders themselves often prefer the binary world of computing to the messy world of humans. Again, MITs Ito made this observation, while Microsofts Tay chatbot disaster proved the point: a nave robot, programmed by binary thinkers in a closed community.

The report acknowledges the industrys problem and recognises that it strongly applies to AI today:

One limitation of AI is the lack of common sense; the ability to judge information beyond its acquired knowledge [] AI is also limited in terms of emotional intelligence.

Then the report makes a simple observation that businesses must take on board: true and complete AI does not exist, it says, adding that there is no evidence yet that it will exist before 2050.

So its a sobering thought that AI software with no common sense and probable bias, and which cant understand human emotions, behaviour, or social contexts, is being tasked with trawling context-free communications data (and even body art) pulled from human society in order to expose criminals, as they are defined by career politicians.

And yet thats precisely whats happening in the US, in the UK, and elsewhere.

The white paper takes pains to set out both the opportunities and limitations of this transformative, trillion-dollar technology, the future of which extends into augmented intelligence and quantum computing. On the one hand, the authors note:

[AI] applications can replace costly human labour and create new potential applications and work along with/for humans to achieve better service standards.

It is certain that AI will play a major role in our future life. As the availability of information around us grows, humans will rely more and more on AI systems to live, to work, and to entertain.

[AI] can achieve impressive results in recognising images or translating speech.

Buton the other hand, they add:

When the system has to deal with new situations when limited training data is available, the model often fails. [] Current AI systems are still missing [the human] level of abstraction and generalisability.

Most current AI systems can be easily fooled, which is a problem that affects almost all machine learning techniques.

Deep neural networks have millions of parameters and to understand why the network provides good or bad results becomes impossible. [] Trained models are often not interpretable. Consequently, most researchers use current AI approaches as a black box.

So organisations should be wary of the black boxs potential to mislead, and to be misled.

The paper has been authored by four leading academics in the field: Dr Guang-Zhong Yang (chair of UK-RAS and a great advocate for the robotics industry), and three of his colleagues at Imperial College, London: Doctors Fani Deligianni, Daniele Ravi, and Javier Andreu Perez. These are clear-sighted idealists as well as world authorities on the subject. As a result, they perhaps under-estimate businesses zeal to slash costs and seek out new, tactical solutions.

The digital business world is faddy and, as anyone who uses LinkedIn knows just as full of surface noise as its consumer counterpart: claims that fail the Snopes test attract thousands of Likes, while rigorous analysis goes unread. As a result, businesses risk seeing the attractions of AI through the pinhole of short-term financial advantage, rather than locating it in a landscape of real social renewal, as academics and researchers do.

As our recent report on UK Robotics Week showed, productivity rather than what this paper calls the amplification of human potential is the main driver of tech policy in government today. Meanwhile, think tanks such as Reform are falling over themselves to praise robotics and AIs shared potential to slash costs and cut humans out of the workforce.

But thats not what AIs designers intend for it at all.

So the problem for the many socially and ethically conscious academics working in the field is that business often leaps before it looks, or thinks. A recent global study by consultancy Avanade found that 70%of the C-level executives it questioned admitted to having given little thought to the ethical dimensions of smart technologies.

But what are the most pressing questions to answer? First, theres the one about human dignity:

Data is the fuel of AI and special attention needs to be paid to the information source and if privacy is breached. Protective and preventive technologies need to be developed against such threats.

It is the responsibility of AI operators to make sure that data privacy is protected. [] Additionally, applications of AI, which may compromise the rights to privacy, should be treated with special legislation that protects the individual.

Then there is the one about human employment. Currently, eight percent of jobs are occupied by robots, claims the report, but in 2020 this percentage will rise to 26.

The authors add:

The accelerated process of technological development now allows labour to be replaced by capital (machinery). However, there is a negative correlation between the probability of automation of a profession and its average annual salary, suggesting a possible increase in short-term inequality.

Id argue that the middle class will be seriously hit by AI and automation. Once-secure, professional careers in banking, finance, law, journalism, medicine, and other fields, are being automated far more quickly than, say, skilled manual trades, many of which will never fall to the machines. (If you want a long-term career, become a plumber.)

But the report continues:

To reduce the social impact of unemployment caused by robots and autonomous systems, the EU parliament proposed that they should pay social security contributions and taxes as if they were human.

(As did Bill Gates.)

Words to make Treasury officials worldwidejump for joy. But whatever the likelihood of such ideas ever being accepted by cost-focused businesses, its clear that strong, national-level engagement is essential to ensure that everyone in society has a clear, factual view of both current and future developments in robotics and AI, says the report not just enterprises and governments.

The reports authors have tried to do just that, and for that we should thank them.

*The two case studies referenced have also been quoted by Prof. Simon Rogerson in a July 2017 article on computer ethics, which Chris Middleton edited and to which he contributed these examples, with Simons permission.

Image credit - Free for use

See the original post:

Navigating the AI ethical minefield without getting blown up - Diginomica

We can all agree that the use of AI in business is at its infancy and may be long until it becomes widespread. Businesses of all sizes may find it easier than thought to run early AI experiments to clear their vision on how to accelerate their competitiveness. However, several myths will be on the way and need to be reflected upon. Lets dive into the most common ones.

AI is humanitys attempt to simulate our brains intuition and put it on the fast track to experience and interpret the world for us. In the early 90s, the development of very narrow applications using AI concepts gave birth to what we now call machine learning (ML). Think of a computer playing checkers or an e-mail spam filter. Deep learning (DL) is making a comeback from its debut in the early 50s. Think of a computer telling you what is in an image or video or translating languages.

In summary, we say that DL is a subset of ML which is a subset of the broad field we call AI.Your business can and eventually will use AI. The reflection about which approach to use will depend on the problem to be solved and the data available.

While there is something magical about predicting an outcome from an input that the computer never saw, the magic ends there. If you try to use machine learning without minimally understanding the problem you want to solve, you will fail miserably. Its very important to think of your AI strategy as a portfolio of approaches to solving very hard problems you cant solve with traditional programming. Each problem may require completely different datasets and approaches to achieve meaningful results.

While its true that whoever has the data will have an advantage in solving certain problems, no business should be trapped in the analysis paralysis around the question do I have enough data? Maybe you dont, but that doesnt mean you shouldnt try to attack a business problem using AI. There are some scenarios to keep in mind:

Most of the machine learning models are trained offline. Surprised? Things can get widely out of control if you just feed more data to your model. By keeping humans in the loop, you can make sure your models will keep performing well.So, every time Siri, Alexa or the Google Assistant tell you they cant help you, but they are learning, it doesnt mean they are learning with you right then. However, the collection of inputs that didnt map to any result is highly valuable data to help you fill the important gaps with users. You will need to use them to retrain your model.

During training, a typical machine learning model will have an accuracy that asymptotically increases with the number of data used to train it. After training, you will test the model with your evaluation set (a subset of the data you had at the beginning) and see how the model performs. You want a model that behaves well with both training data and new data.Sometimes an accuracy above 70% will be more than enough for practical applications as long as you have a good plan to work out the situations where the model doesnt work well and improve your model over time.

The image above is from a mobile application that implements the imagenet model for image recognition. As you can see, the photo on the left, from above the mouse, led to an unexpected result. By tilting the camera I managed to catch the right category, albeit at a small confidence percentage.Now imagine if the mobile application used the device sensor information like gyroscope data, and it told me that I should tilt the camera in order to get a better result. It wouldve guided me to a better experience because it wouldve provided the machine learning model a better input.Depending on how you design your application, you can also get valuable information from users that will help improve your model.

The cost of building your first AI project should be equivalent to the cost you had when you built your first mobile app, just to give you a tangible reference.In contrast, the cost of not building your first AI project soon, rest assured, will be much higher as time goes by.

Companies who will treat AI as part of their portfolio of problem-solving tools will probably achieve compounding gains over time. They will have, however, to manage internal expectations around early results and consider experiments as bets worth making.

Mars Cyrillo is the VP of Machine Learning and Product Development at CI&T,a digital tech agency.

Above: The Machine Intelligence Landscape This article is part of our Artificial Intelligence series. You can download a high-resolution version of the landscape featuring 288 companies by clicking the image.

View post:

7 myths about AI that are holding your business back - VentureBeat

The future of baby-making is set to be very different from the one we have now. Just last week, a researcher boldly claimed that growing embryos in a laboratory setting will become far more commonplace, and will allow us to remove genetic diseases from the equation before the baby is born.

Now, during the annual meeting of the European Society of Human Reproduction and Embryology in Geneva, scientists have given us yet another peek into the future of conception. In a groundbreaking new study, a team of embryologists was pitted against an artificial intelligence (AI) during simulated in vitro fertilization (IVF) selection process and the AI appeared to be better at selecting viable embryos.

During IVF, an egg is removed from the hopeful mothers ovaries and fertilized with the potential fathers sperm in a laboratory setting. This fertilized egg is then implanted in the womans womb and allowed to develop normally.

Its used for those with fertility problems, and currently has variable rates of success. Sometimes, the embryos fail for a variety of reasons, and experts are trained to look out for defects that may trigger a failed pregnancy. Between 30 to 60 percent of seemingly viable embryos fail to implant in the uterus.

This new study a collaborative effort between So Paulo State University and Londons Boston Place Clinic decided to pit experts against an AI designed to do their jobs for them. Using bovine embryos, the AI was given a chance to train itself to look for viable embryos and highlight defective ones.

Both the AI and a team of embryologists were then given 48 examples of bovine embryos to look at, and had a chance to observe them three times over.

Using just 24 key characteristics, such as morphology, texture, and the quantity and quality of the cells present, the AI was able to pick viable embryos 76 percent of the time. Although the accuracy value for the embryologists was not given, it was said to be lower; importantly, unlike the AI, the embryologists found it difficult getting a consensus on the quality of the embryos.

See the article here:

Artificial Intelligence Better Than Medical Experts At Choosing Viable IVF Embryos - IFLScience

Growing concerns about how artificial intelligence (AI) makes decisions has inspired U.S. researchers to make computers explain their thinking.

Computers are going to become increasingly important parts of our lives, if they arent already, and the automation is just going to improve over time, so its increasingly important to know why these complicated systems are making the decisions that they are, assistant professor of computer science at the University of California Irvine, Sameer Singh, told CTVs Your Morning on Tuesday.

Singh explained that, in almost every application of machine learning and AI, there are cases where the computers do something completely unexpected.

Sometimes its a good thing, its doing something much smarter than we realize, he said. But sometimes its picking up on things that it shouldnt.

Such was the case with the Microsoft AI chatbot, Tay, which became racist in less than a day. Another high-profile incident occurred in 2015, when Googles photo app mistakenly labelled a black couple as gorillas.

Singh says incidents like that can happen because the data AI learns from is based on humans; either decisions humans made in the past or basic social-economic structures that appear in the data.

When machine learning models use that data they tend to inherit those biases, said Singh.

In fact, it can get much worse where if the AI agents are part of a loop where theyre making decisions, even the future data, the biases get reinforced, he added.

Researchers hope that, by seeing the thought process of the computers, they can make sure AI doesnt pick up any gender or racial biases that humans have.

However, Googles research director Peter Norvig cast doubt on the concept of explainable AI.

You can ask a human, but, you know, what cognitive psychologists have discovered is that when you ask a human youre not really getting at the decision process. They make a decision first, and then you ask, and then they generate an explanation and that may not be the true explanation, he said at an event in June in Sydney, Australia.

So we might end up being in the same place with machine learning where we train one system to get an answer and then we train another system to say given the input of this first system, now its your job to generate an explanation.

Norvig suggests looking for patterns in the decisions themselves, rather than the inner workings behind them.

But Singh says understanding the decision process is critical for future use, particularly in cases where AI is making decisions, like approving loan applications, for example.

Its important to know what details theyre using. Not just if theyre using your race column or your gender column but are they using proxy signals like your location, which we know it could be an indicator of race or other problematic attributes, explained Singh.

Over the last year theres been multiple efforts to find out how to better explain the rational of AI.

Currently, The Defense Advanced Research Projects Agency (DARPA) is funding 13 different research groups, which are pursuing a range of approaches to making AI more explainable.

The rest is here:

Explainable AI: The push to make sure machines don't learn to be racist - CTV News

NASAs Frontier Development Lab (FDL), a public-private research institute operated jointly by the space agencys Ames Research Center and the SETI Institute, announced it will use artificial intelligence to study methods of protecting the Earth from potentially hazardous asteroids and comets.

The announcement was made on Friday, June 30, designated in 2014 as International Asteroid Day, an annual event that addresses potential threats from Near Earth Objects (NEOs).

June 30 was chosen because it is the anniversary of the 1908 Tunguska impact, when an asteroid estimated to have been 120 feet wide exploded over the Stony Tunguska River in Siberia.

The annual commemoration is the brainchild of astrophysicist and Queen lead guitarist Brian May and film director Grigorij Richters.

Several years ago, Richters directed 51 Degrees North, a film depicting a fictional asteroid strike in London.

For this years event, FDL assembled a research team to discuss the ways artificial intelligence can assist in planetary defense. In addition to addressing the issue of potentially hazardous asteroids and comets, the researchers also dealt with the possible threat from solar storms.

Now in its second year, FDL partners with various private and academic organizations, including Luxembourg Space Resources, Lockheed Martin, IBM, Intel, Nvidia, and various other corporations.

Using an interdisciplinary approach, FDL brings together machine learning with scholars in a diversity of fields, including planetary science and heliophysics.

Grand challenges like planetary defense require ingenious approaches, said FDL Director James Parr. We wanted to create a platform that industrializes breakthrough work useful to the space program and the task of protecting our planet.

Researchers at the conference discussed options such as using machine learning to model the orbits of long period comets, automating 2D research data into 3D images of asteroids to identify their spin rates and shapes, using data mining to further study space weather produced by interactions between the Sun and the Earth, utilizing machine learning to provide early warnings of solar storms, and merging machine learning and other data to search for water sources on the Moon.

Laurel Kornfeld is a freelance writer and amateur astronomer from Highland Park, NJ, who enjoys writing about astronomy and planetary science. She studied journalism at Douglass College, Rutgers University, and earned a Graduate Certificate of Science in astronomy from Swinburne Universitys Astronomy Online program.

Read more:

NASA will use artificial intelligence for planetary defense - The Space Reporter