Category Archives: Artificial Intelligence
Winners and losers in the fulfilment of national artificial intelligence aspirations – Brookings Institution
The quest for national AI success has electrified the worldat last count, 44 countries have entered the race by creating their own national AI strategic plan. While the inclusion of countries like China, India, and the U.S. are expected, unexpected countries, including Uganda, Armenia, and Latvia, have also drafted national plans in hopes of realizing the promise. Our earlier posts, entitled How different countries view artificial intelligence and Analyzing artificial intelligence plans in 34 countries detailed how countries are approaching national AI plans, as well as how to interpret those plans. In this piece, we go a step further by examining indicators of future AI needs.
Clearly, having a national AI plan is a necessary but not sufficient condition to achieve the goals of the various AI plans circulating around the world; 44 countries currently have such plans. In previous posts, we noted how AI plans were largely aspirational, and that moving from this aspiration to successful implementation required substantial public-private investments and efforts.
In order to analyze the implementation to-date of countries national AI objectives, we assembled a country-level dataset containing: the number and size of supercomputers in the country as a measure of technological infrastructure, the amount of public and private spending on AI initiatives, the number of AI startups in the country, the number of AI patents and conference papers the countrys scholars produced, and the number of people with STEM backgrounds in the country. Taken together, these elements provide valuable insights as to how far along a country is in implementing its plan.
As analyzing each of the data elements individually presented some data challenges, we conducted a factor analysis to determine if there was a logical grouping of the data elements. Factor analysis reveals the underlying structure of data; that is, the technique mathematically determines how many groups (or factors) of data exist by analyzing which data elements are most closely related to other elements.
Given that our data included five distinct dimensions (i.e., technology infrastructure, AI startups, spending, patents and conference papers, and people), we expected that five factors would emerge, particularly since the dimensions appear to be relatively separate and distinct. But the data showed otherwise. In all, this factor analysis revealed all of the data elements fall under two factorspeople-related and technology-related.
The first factor is the set of AI hiring, STEM graduates, and technology skill penetration data points, which are all associated with the people side of AI. Without qualified people, AI implementations are unlikely to be effective.
The second factor is comprised of all the non-people data elements of AI, which include computing power, AI startups, investment, conference and journal papers, and AI patent submission data points. In looking at these data elements, we realized that all of the data elements in this factor were technology-related, either from a hardware or a thought-leadership standpoint.
Given these findings, we can treat the data as containing two distinct categories: people and technology. Figure 1 shows where a select set of countries sit along these dimensions.
The countries that are in the upper right-hand corner we dub Leaders; they have both the people (factor 1) and the technology (factor 2) to meet their goals. Countries in the lower right quadrant we dub Technically Prepared, and because they are higher on the technology dimensions (factor 2) but lower on the people dimensions (factor 1). Those countries in the upper left quadrant we dub the People Prepared, and largely due to their factors higher on the people dimension (factor 1), but lower on the technology dimension (factor 2). The final quadrantthe lower left quadrantwe dub the Aspirational quadrant since those countries have not yet substantially moved forward in either the people or technology dimension (factor 1 and 2 respectively) in achieving their national AI strategy.
China is unmistakably closer to achieving its national AI strategy goals. It is both a leader in the technical dimension and a leader in the people dimension. Of note is that, while China is strongly positioned in both dimensions, it is not highest in either dimension; the U.S. is higher in the technical dimension, and India, Singapore, and Germany are all higher on the people dimension. Given the population of China and its overall investment in AI-related spending, it is not surprising that China has an early and commanding lead over other countries.
The U.S., while a leader in the technology dimension, particularly in the sub-dimensions of investments and patents, ranks a relatively dismal 15th place after such countries as Russia, Portugal, and Sweden in the people dimension. This is especially clear in the sub-dimension of STEM graduates, where it ranks near the bottom. While the vast U.S. spending advantage has given it an early lead in the technology dimensions, we suspect that the overall lack of STEM-qualified individuals is likely to significantly constrain the U.S. in achieving its strategic goals in the future.
By contrast, India holds a small but measurable lead over other countries in the people dimension, but is noticeably lagging in the technology dimension, particularly in the investment sub-dimension. This is not surprising, as India has long been known for its education prowess but has not invested equally with leaders in the technology dimension.
Our focus on China, the U.S., and India is not to suggest that these are the only countries that can achieve their national AI objectives. Other countries, notably South Korea, Germany, and the United Kingdom are just outside of top positions, and, by virtue of generally being well-balanced between the people and the technology dimensions, have an excellent chance to close the gap
At present, China, the U.S., and India are leading the way in implementing national AI plans. Yet China has already hit on a balanced strategy that has thus far eluded the U.S. and India. This suggests that China needs to merely continue its strategy. However, strategy refinement is necessary for the U.S. and India to keep pace. These leaders are closely followed by South Korea, Germany, and the United Kingdom.
In future posts, we will dive deeper into both the people and technology dimensions, and will dissect specific shortfalls for each country, as well as what can be done to address these shortfalls. Anything short of a substantial national commitment to AI achievement is likely to relegate the country to the status of a second-tier player in the space. If the U.S. wants to dominate this space, it needs to improve the people dimension of technology innovation and make sure it has the STEM graduates required to push its AI innovation to new heights.
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Winners and losers in the fulfilment of national artificial intelligence aspirations - Brookings Institution
Artificial Intelligence Has Found an Unknown ‘Ghost’ Ancestor in The Human Genome – ScienceAlert
Nobody knows who she was, just that she was different: a teenage girl from over 50,000 years ago of such strange uniqueness she looked to be a 'hybrid' ancestor to modern humans that scientists had never seen before.
Only recently, researchers have uncovered evidence she wasn't alone. In a 2019 study analysing the complex mess of humanity's prehistory, scientists used artificial intelligence (AI) to identify an unknown human ancestor species that modern humans encountered and shared dalliances with on the long trek out of Africa millennia ago.
"About 80,000 years ago, the so-called Out of Africa occurred, when part of the human population, which already consisted of modern humans, abandoned the African continent and migrated to other continents, giving rise to all the current populations", explainedevolutionary biologist Jaume Bertranpetit from the Universitat Pompeu Fabra in Spain.
As modern humans forged this path into the landmass of Eurasia, they forged some other things too breeding with ancient and extinct hominids from other species.
Up until recently, these occasional sexual partners were thought to include Neanderthals and Denisovans, the latter of which were unknown until 2010.
But in this study, a third ex from long ago was isolated in Eurasian DNA, thanks to deep learning algorithms sifting through a complex mass of ancient and modern human genetic code.
Using a statistical technique called Bayesian inference, the researchers found evidence of what they call a "third introgression" a 'ghost' archaic population that modern humans interbred with during the African exodus.
"This population is either related to the Neanderthal-Denisova clade or diverged early from the Denisova lineage," the researchers wrote in their paper, meaning that it's possible this third population in humanity's sexual history was possibly a mix themselves of Neanderthals and Denisovans.
In a sense, from the vantage point of deep learning, it's a hypothetical corroboration of sorts of the teenage girl 'hybrid fossil' identified in 2018;although there's still more work to be done, and the research projects themselves aren't directly linked.
"Our theory coincides with the hybrid specimen discovered recently in Denisova, although as yet we cannot rule out other possibilities", one of the team, genomicist Mayukh Mondal from the University of Tartu in Estonia, said in a press statement at the time of discovery.
That being said, the discoveries being made in this area of science are coming thick and fast.
Also in 2018, another team of researchers identified evidence of what they called a "definite third interbreeding event" alongside Denisovans and Neanderthals, and a pair of papers published in early 2019 traced the timeline of how those extinct species intersected and interbred in clearer detail than ever before.
There's a lot more research to be done here yet. Applying this kind of AI analysis is a decidedly new technique in the field of human ancestry, and the known fossil evidence we're dealing with is amazingly scant.
But according to the research, what the team has found explains not only a long-forgotten process of introgression it's a dalliance that, in its own way, informs part of who we are today.
"We thought we'd try to find these places of high divergence in the genome, see which are Neanderthal and which are Denisovan, and then see whether these explain the whole picture," Bertranpetit told Smithsonian.
"As it happens, if you subtract the Neanderthal and Denisovan parts, there is still something in the genome that is highly divergent."
The findings were published in Nature Communications.
A version of this article was originally published in February 2019.
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Artificial Intelligence Has Found an Unknown 'Ghost' Ancestor in The Human Genome - ScienceAlert
Artificial Intelligence Trends and Predictions for 2021 | AI Trending Now – Datamation
Artificial intelligence (AI) has taken on many new shapes and use cases as experts learn more about whats possible with big data and smart algorithms.
Todays AI market, then, consists of a mixture of tried-and-true smart technologies with new optimizations and advanced AI that is slowly transforming the way we do work and live daily life.
Read on to learn about some artificial intelligence trends that are making experts most excited for the future of AI:
More on the AI market: Artificial Intelligence Market
With its ability to follow basic tasks and routines based on smart programming and algorithms, artificial intelligence is becoming embedded in the way organizations automate their business processes.
AIOps and MLops are common use cases for AI and automation, but the breadth and depth of what AI can automate in the enterprise is quickly growing.
Bali D.R., SVP at Infosys, a global digital services and consulting firm, believes that AI is moving toward a certain level of hyper-automation, partially in response to the unexpected changes in manual data and procedures caused by the pandemic.
We are in the second inflection point for AI as it graduates from consumer AI, towards enterprise-grade AI, D.R. said. Being exposed to an over-reliance on manual procedures, such as mass rescheduling in the airline industry, unprecedented loan applications in banks, etc., the industries are now turning to hyper-automation that combines robotic process automation with modern machine learning to ensure they can better handle surges in the future.
Although AI automation is still mostly limited to interval and task-oriented automation that requires little imagination or guesswork on the part of the tool, some experts believe we are moving closer to more applications for intelligent automation.
David Tareen, director for artificial intelligence at SAS, a top analytics and AI software company, had this to say about the future of intelligent automation:
Intelligent automation is an area I expect to grow, Tareen said. Just like we automated manufacturing work, we will use AI heavily to automate knowledge work.
The complexity comes in because knowledge work has a high degree of variability. For example, an organization will receive feedback on their products or services in different ways and often in different languages as well. AI will need to ingest, understand, and modify processes in real-time before we can automate knowledge work at large.
AI, automation, and the job market: Artificial Intelligence and Automation
Because of the depth of big data and AIs reliance on it, theres always the possibility that unethical or ill-prepared data will make it into an AI training data set or model.
As more companies recognize the importance of creating AI that conducts its operations in a compliant and ethical manner, a number of AI developers and service providers are starting to offer responsible AI solutions to their customers.
Read Maloney, SVP of marketing at H2O.ai, a top AI and hybrid cloud company, explained what exactly responsible AI is and some of the different initiatives that companies are undertaking to improve their AI ethics.
AI creates incredible new opportunities to improve the lives of people around the world, Maloney said. We take the responsibility to mitigate risks as core to our work, so building fairness, interpretability, security, and privacy into our AI solutions is key.
Maloney said the market is seeing an increased adoption of the core pillars of responsible AI, which he shared with Datamation:
Companies are exploring several ways to make their AI more responsible, and most are starting with cleaning and assessing both data sets and existing AI models.
Brian Gilmore, director of IoT product management at InfluxData, a database solutions company, believes that one of the top options for model and data set management is distributed ledger technology (DLT).
As attention builds around the ethical and cultural impact of AI, some organizations are beginning to invest in ancillary but important technologies that utilize consensus and other trust-ensuring systems as a part of the AI framework, Gilmore said. For example, distributed ledger technology provides a sidecar platform for auditable proof of integrity for models and training data.
The decentralized ownership, distribution of access, and shared accountability of DLT can bring significant transparency to AI development and application across the board. The dilemma is whether for-profit corporations are willing to participate in a community model, trading transparency for consumer trust in something as mission critical as AI.
See more: The Ethics of Artificial Intelligence (AI)
Up to this point, AI has most frequently been used to optimize business processes and automate some home routines for consumers.
However, some experts are beginning to realize the potential that AI-powered models can have for solving global issues.
Read Maloney at H2O.ai has worked with people from a variety of industries to brainstorm how AI can be used for the greater good.
We work with many like-minded customers, partners, and organizations tackling issues from education, conservation, health care, and more, Maloney said. AI for good is fundamental to not only our work, including current work on climate change, wildfires, and hurricane predictions, but we are seeing more and more AI for good work to make the world a better place across the AI industry.
Some of the most exciting applications of altruistic AI are being implemented in early education right now.
For instance, Helen Thomas, CEO ofDMAI, an AI-powered health care and education company, offers an AI-powered product to ensure that preschool-aged children are getting the education they need, despite potential pandemic setbacks:
On top of pre-existing barriers to preschool education, including cost and access, recent research findings suggest children born during the COVID-19 pandemic display lower IQ scores than those born before January 2020, which means toddlers are less prepared for school than ever before.
DMAI DBA Animal Island Learning Adventure (AILA) is changing this with AI. [Our product] harnesses cognitive AI to deliver appropriate lessons in a consistent and repetitious format, supportive of natural learning patterns
Recognizing learning patterns that parents might miss, the AI creates an adaptive learning journey and doesnt allow the child to move forward until theyve mastered the skills and concepts presented. This intentional delivery also increases attention span over time, ensuring children step into the classroom with the social-emotional intelligence to succeed.
More on this topic: How AI is Being Used in Education
Internet of Things (IoT) devices have become incredibly widespread among both enterprise and personal users, but what many tech companies still struggle with is how to gather actionable insights from the constant inflow of data from these devices.
AIoT, or the idea of combining artificial intelligence with IoT products, is one field that is starting to address these pools of unused data, giving AI the power to translate that data quickly and intelligently.
Bill Scudder, SVP and AIoT general manager at AspenTech, an industrial AI solutions company, believes that AIoT is one of the most crucial fields for enabling more intelligent, real-time business decisions.
Forrester has noted that up to 73% of all data collected within the enterprise goes unused, which highlights a critical challenge with IoT, Scudder said. As the volume of connected devices for example, in industrial IoT settings continues to increase, so does the volume of data collected from these devices.
This has resulted in a trend seen across many industries: the need to marry AI and IoT. And heres why: where IoT allows connected devices to create and transmit data from various sources, AI can take that data one step further, translating data into actionable insights to fuel faster, more intelligent business decisions. This is giving way to the rising trend of artificial intelligence of things or AIoT.
Decision intelligence (DI) is one of the newest artificial intelligence concepts that takes many current business optimizations a step farther, by using AI models to analyze wide-ranging sets of commercial data. These analyses are used to predict future outcomes for everything from products to customers to supply chains.
Sorcha Gilroy, data science team lead at Peak, a commercial AI solutions provider, explained that although decision intelligence is a fairly new concept, its already gaining traction with larger enterprises because of its detailed business intelligence (BI) offerings.
Decision intelligence is a new category of software that facilitates the commercial application of artificial intelligence, providing predictive insight and recommended actions to users, Gilroy said. It is outcome focused, meaning a solution must deliver against a business need before it can be classed as DI.
Recognized by Gartner and IDC, it has the potential to be the biggest software category in the world and is already being utilized by businesses across a variety of use cases, from personalizing shopper experiences to streamlining complex supply chains. Brands such as Nike, PepsiCo, and ASOS are known to be using DI already.
Read next: Top Performing Artificial Intelligence Companies
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Artificial Intelligence Trends and Predictions for 2021 | AI Trending Now - Datamation
Artificial Intelligence in the Legal Field: – Lexology
Artificial Intelligence is a mechanism through which computers are programmed to undertake tasks which otherwise are done by the human brain. Like every other thing, it also has pros and cons to it. While the usage of Artificial Intelligence can help in completing a task in a few minutes on the other hand, if it worked as well as it is deemed to, it could potentially take away employment of thousands of people across the country. The growing influence of Artificial Intelligence (AI) can be seen across various industries, from IT to farming, from manufacturing to customer service. The Indian Legal Industry, meanwhile, has always been a little slower to adapt to technology and has seen minimal changes to superior technology. This is promulgated by several lawyers still feeling comfortable with the same old system of functioning that was designed decades ago. AI has managed to disrupt other industries. W ith an ever growing pendency and increasing demand for self-service systems even in the legal fraternity, this once assumed-to-be utopian idea can become a reality for all lawyers. Some of the concerning questions that will be addressed in this article are as follows:
What are the changes that the Indian legal system has already witnessed?
The Introduction of AI into the legal system has made a drastic impact on the legal fraternities across the globe. The first global player to attempt using AI for legal purposes was through the IBM Watson powered robot ROSS, which used a unique method by mining data and interpreting trends and patterns in the law to solve research questions. Interestingly, the area that will get most affected is not the litigation process or arbitration matters, but in fact the back-end work for the litigation and arbitration purposes such as research, data storage and usage, etc.
Due to the sheer volume of cases and diversity in case matters, the Indian laws and their interpretations keep changing and developing further. If lawyers could have access to AI-Based technology that could help with research matters then the labour cost of research work could be significantly reduced, leading to the profitability and significant increase in the speed of getting work done. While this could lead to the reduction of staff members, i. e. Paralegals and some associates, it would also increase the overall productivity for all lawyers and lead to the fast-tracking of legal research and drafting.
One of the best examples is the usage of the AI-based Software Kira by Cyril Amarchand Mangaldas that examines, identifies and provides a refined search on the specific data needed with a reportedly high degree of precision. This reportedly has allowed the firm to focus on more important aspects of the litigation process and has reduced the repetitive and monotonous work usually done by paralegals, interns and other entry-level employees.
In fact, several noted Jurists and Judges have spoken in good terms about the necessity of such AI-Based software that could be useful for the docketing system and simple decision making process. Some of the statements made by these eminent personalities are as follows:
Justice SA Bobde had said : We must increasingly focus on harnessing IT and IT enabled services (ITES) for providing more efficient and cost-effective access to and delivery of justice. This must also include undertaking serious study concerning the future of Artificial Intelligence in law, especially how Artificial Intelligence can assist in judicial decision making. I believe exploring this interface would be immensely beneficial for many reasons. For instance, it would allow us to streamline courts caseloads through enabling better court management. This would be a low hanging fruit. On the other end of the spectrum, it will allow us to shift the judicial time from routine-simple-straightforward matters (e.g. cases which are non-rivalrous) and apply them to more complex-intricate matters that require more human attention and involvement.Therefore, in India identification of such matters and developing relevant technology ought to be our next focus.
Justice DY Chandrachud said : The idea of Artificial Intelligence is not to supplant the human brain or the human mind or the presence of judges but to provide a facilitative tool to judges to reassess the processes which they follow, to reassess the work which they do and to ensure that their outcome are more predictable and consistent and ultimately provide wider access to justice to the common citizens.
What legal problems can AI solve in India?
While the country admittedly has a massive issue with respect to its judicial system owing to the massive pendency and huge volume of unresolved cases, the inclusion of AI can help with resolving a majority of its problems. The introduction of technological advancement will aid the lawyers in conducting legal research in an efficient and timely manner and thus will ensure AI software equipped lawyers to focus more on advising their clients and taking up complex issues/cases. It also helps in assessing a potential outcome to pending cases and could be of great assistance to the courts and private parties to help them decide on which cases to pursue, which ones to resolve amicably if possible and which ones to let go of!
Some of the benefits of implementing the nation-wide use of AI systems are as follows:
What are the changes needed for the AI systems in India and the road ahead?
While there are several benefits to Lawyers/Firms and the Judiciary for implementing AI into the Legal fraternity, there are a few caveats as well. With any form of technology for the matter, the risk of data infringement, cyber-attacks and hacking attempts are a constant threat. Incorrect software is also an issue that has often been a question over technology, especially over those technologies that are relatively untested and new in the market.
There are also some questions regarding the nature of ethics of an AI. An important point to keep in mind is that Artificial Intelligence software does not have a mind of their own. Although they do think before taking an action, their actions are completely programmed and there is always an issue of trustworthiness as AI needs to have a defined ethical purpose and technically robust and reliable systems. These issues were also seen to persist in the highly acclaimed ROSS, which saw several glitches.
There is also another issue that arises with implementing Artificial Intelligence. The affordability of these AI software is a factor that needs deliberation. The maintenance of these AI facilities are an added concern, with firms investing in privatised AI research facilities as mentioned earlier. Thus the investment that would be required to establish and operate would be expensive, thus making a division of technological capabilities ab initio. This is also taking into factor the unknown probability of the learning curve involved in dealing with the lawyers, firms and judiciary members who utilize such technology.
With these challenges kept in mind, the regulations with respect to AI use must be kept in mind, particularly with respect to how the judiciary uses it. There has and there always will be a sense of mistrust in technologies such as these, but the progress needs to be made slowly and cannot be drastically at this point, without understanding its legal, financial and security implications. The following actions must be taken when the usage of AI is eventually implemented:
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Artificial Intelligence in the Legal Field: - Lexology
They create an artificial intelligence model capable of making ethical judgments, but their advice is racis… – Market Research Telecast
The Allen Institute for Artificial Intelligence and the Paul G. Allen School of Computer Science and Engineering at the University of Washington, both in the USA, developed a machine learning model called Delphi, capable of making ethical judgments about a great variety of everyday situations.
The artificial intelligence project was launched on October 14, along with an article that describe its construction, and can be consulted through the portal Ask Delphi (Ask Delphi, in Spanish). It is very simple to use and you just have to enter and write in its question bar almost any question about a real life situation so that the algorithm reflect and decide if its something bad, acceptable or good from the moral and ethical point of view. Each answer can be shared on Twitter.
Since then, Delphi has attracted attention and has been the talk of social media, not precisely because of the good advice it offers, but because of its many moral errors and strange judgments. Some of its users have shown that what they conclude can actually be racist. A netizen asked him what he thought about a white man walking towards you at night and he replied, Okay. However, when changing the subject to black man the answer was: It is worrisome.
According to the portal Futurism, the model presented a greater number of judgment rulings in the first days after being online. Initially it included a tool that allowed users to compare two situations to find out which of the two was more or less morally acceptable, but it was disabled after generating particularly offensive responses and even homophobic. For example, being heterosexual is more morally acceptable than being gay.
Not all the ethical judgments Delphi offers are wrong, but the way you pose a dilemma can change your system of moral reasoning. After testing the model many times, eventually it is understood that it is easy to influence artificial intelligence to obtain practically any answer you want.
For Delphi, its rude to listen to loud music at three in the morning while our roommate is sleeping, but adding to the question: if that makes me happy, her opinion changes to: okay . According to the portal The Verge, if you add the phrase if it makes everyone happy at the end of a question, artificial intelligence will be benevolent against any immoral action, including genocide.
Although the Delphi authors made it available to the general public to demonstrate what cutting edge models can achieve today, they also caution that the results could be potentially offensive / troublesome / harmful. Delphi demonstrates both the promises and limitations of language-based neural models when taught with ethical judgments made by people, they stress.
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They create an artificial intelligence model capable of making ethical judgments, but their advice is racis... - Market Research Telecast
Artificial Intelligence project aims to improve standards and development of AI systems – University of Birmingham
A new project has been launched in partnership with the University of Birmingham aiming to address racial and ethical health inequalities using artificial intelligence (AI).
STANDING Together, being led by University Hospitals Birmingham NHS Foundation Trust (UHB), aims to develop standards for datasets that AI systems use, to ensure they are diverse, inclusive and work across all demographic groups. The resulting standards will help regulators, commissioners, policymakers and health data institutions assess whether AI systems are underpinned by datasets that represent everyone, and dont leave underrepresented or minority groups behind.
Xiao Liu, Clinical Researcher in Artificial Intelligence and Digital Healthcare at the University of Birmingham and UHB, and STANDING Together project co-leader, said: Were looking forward to starting work on our project, and developing standards that we hope will improve the use of AI both in the UK and around the world. We believe AI has enormous potential to improve patient care, but through our earlier work on producing AI guidelines, we also know that there is still lots of work to do to make sure AI is a success stories for all patients. Through the STANDING Together project, we will work to ensure AI benefits all patients and not just the majority.
NHSX NHS AI Lab, the NIHR, and the Health Foundation have awarded in total 1.4m to four projects, including STANDING Together. The other organisations working with UHB and the University of Birmingham on STANDING Together are the Massachusetts Institute of Technology, Health Data Research UK, Oxford University Hospitals NHS Foundation Trust, and The Hospital for Sick Children (Sickkids, Toronto).
The NHS AI Lab introduced the AI Ethics Initiative to support research and practical interventions that complement existing efforts to validate, evaluate and regulate AI-driven technologies in health and care, with a focus on countering health inequalities. Todays announcement is the result of the Initiatives partnership with The Health Foundation on a research competition, enabled by NIHR, to understand and enable opportunities to use AI to address inequalities and to optimise datasets and improve AI development, testing and deployment.
Brhmie Balaram, Head of AI Research and Ethics at NHSX, said: We're excited to support innovative projects that demonstrate the power of applying AI to address some of our most pressing challenges; in this case, we're keen to prove that AI can potentially be used to close gaps in minority ethnic health outcomes. Artificial intelligence has the potential to revolutionise care for patients, and we are committed to ensuring that this potential is realised for all patients by accounting for the health needs of diverse communities."
Dr Indra Joshi, Director of the NHS AI Lab at NHSX, added: As we strive to ensure NHS patients are amongst the first in the world to benefit from leading AI, we also have a responsibility to ensure those technologies dont exacerbate existing health inequalities.These projects will ensure the NHS can deploy safe and ethical Artificial Intelligence tools that meet the needs of minority communities and help our workforce deliver patient-centred and inclusive care to all.
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Artificial Intelligence project aims to improve standards and development of AI systems - University of Birmingham
Filings buzz in the railway industry: Increase in artificial intelligence mentions – Railway Technology
Mentions of artificial intelligence within the filings of companies in the railway industry rose 64% between the first and second quarters of 2021.
In total, the frequency of sentences related to artificial intelligence between July 2020 and June 2021 was 137% increase than in 2016 when GlobalData, from whom our data for this article is taken, first began to track the key issues referred to in company filings.
When companies in the railway industry publish annual and quarterly reports, ESG reports and other filings, GlobalData analyses the text and identifies individual sentences that relate to disruptive forces facing companies in the coming years. Artificial intelligence is one of these topics - companies that excel and invest in these areas are thought to be better prepared for the future business landscape and better equipped to survive unforeseen challenges.
To assess whether artificial intelligence is featuring more in the summaries and strategies of companies in the railway industry, two measures were calculated. Firstly, we looked at the percentage of companies which have mentioned artificial intelligence at least once in filings during the past twelve months - this was 78% compared to 52% in 2016. Secondly, we calculated the percentage of total analysed sentences that referred to artificial intelligence.
Of the 50 biggest employers in the railway industry, Hitachi Transport System, Ltd. was the company which referred to artificial intelligence the most between July 2020 and June 2021. GlobalData identified 83 artificial intelligence-related sentences in the Japan-based company's filings - 2.4% of all sentences. XPO Logistics Inc mentioned artificial intelligence the second most - the issue was referred to in 1.3% of sentences in the company's filings. Other top employers with high artificial intelligence mentions included East Japan Railway Co, Yamato Holdings Co Ltd and ID Logistics Group.
Across all companies in the railway industry the filing published in the second quarter of 2021 which exhibited the greatest focus on artificial intelligence came from XPO Logistics Inc. Of the document's 1,093 sentences, 11 (1%) referred to artificial intelligence.
This analysis provides an approximate indication of which companies are focusing on artificial intelligence and how important the issue is considered within the railway industry, but it also has limitations and should be interpreted carefully. For example, a company mentioning artificial intelligence more regularly is not necessarily proof that they are utilising new techniques or prioritising the issue, nor does it indicate whether the company's ventures into artificial intelligence have been successes or failures.
GlobalData also categorises artificial intelligence mentions by a series of subthemes. Of these subthemes, the most commonly referred to topic in the second quarter of 2021 was 'smart robots', which made up 82% of all artificial intelligence subtheme mentions by companies in the railway industry.
By Andrew Hillman.
Methodology:
GlobalDatas unique Job analytics enables understanding of hiring trends, strategies, and predictive signals across sectors, themes, companies, and geographies. Intelligent web crawlers capture data from publicly available sources. Key parameters include active, posted and closed jobs, posting duration, experience, seniority level, educational qualifications and skills.
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Filings buzz in the railway industry: Increase in artificial intelligence mentions - Railway Technology
5 Steps to Help Tech Companies Reduce Bias in AI – Entrepreneur
Opinions expressed by Entrepreneur contributors are their own.
Children inevitably adapt to the culture in which they were raised. Parents or guardians shape the lens through which they view the world, largely through the examples they set. Many parents experience humored horror when a child picks up on an inappropriate word, likely from an overheard adult conversation, and begins to employ that expression in their everyday speech. It does not matter whether the parent is intentionally or unintentionally crafting the lens for the childthey will still pick up on the parents viewpoints and habits.
We are witnessing this same progression in the tech world. Artificial intelligence systems adopt the worldview of their creators, just like children adopt the worldview of the adults that raised them. This is problematic because artificial intelligence cannot develop its own worldview, learn from its life experiences or challenge its creators worldview like a child might as they grow older.
Here are five initiatives that will help prevent bias in tech.
Artificial intelligence systems are biased, and the technology usually follows the viewpoints of its creators. While society has changed considerably in the last half-century, corporations still have underlying biases (whether they realize them or not). Its essential that we take active steps to reverse our biases so that we can prevent further biases from developing in artificial intelligence, and the best way to do this is to make the tech industry more accessible to a wider range of people.
Initiatives like Girls Who Code, AI4ALLand other educational programs make it possible for children to develop an interest in technology. To reduce bias and make the tech industry more diverse, leaders must invest in the education of young people so that they can develop an interest in the field and build the skills necessary to pursue a career. Tech companies should invest in a range of students early on, knowing that investments in education yield long-term results.
Related:This Is the Most PowerfulArtificial IntelligenceTool in the World
Despite numerous call-outs of major industry leaders, the tech world still lacks diversity. The Harvard Business Review reported that leading companies like Google have only crawled ahead toward more diversity among staff members. Even nearly seven years after tech companies started reporting diversity efforts, most leading tech organizations are failing with minorities only making up single-digit percentages of the overall workforce.
It goes without saying, but companies must actively hire and promote with diversity in mind. This is the most effective way to eliminate biases in artificial intelligence, because with a more diverse workforce, AI will be developed with multi-faceted viewpoints and experiences in mind. And we cant just do it at the entry-level diversity must extend to the top of technological leadership so that those who sign off on projects can see blind spots in the systems.
Bias is already in your data sets, and you shouldnt ignore it. To counter biases, every AI technology developer should devote time to evaluating the data sets with which the system was created. This evaluation should take place at every stage of development, from the initial design to the final proofs.
The best way to evaluate AI for biases is to ask specific questions. The FTC provides guidelines to determine if artificial intelligence is on the right trajectory, and to clarify what is allowed (or prohibited) by law. Developers must question themselves and the technology they are creating. It is imperative that developers understand their own biases especially the unconscious ones and can evaluate their work for the same. Working to eliminate biases is not a linear process, as it will take multiple back-and-forth steps.
Related:What Every Entrepreneur Must Know AboutArtificial Intelligence
Rigorous evaluations cant stop at data sets. Technology is growing and changing at such a rapid pace, and strategies, systemsand even outcomes should be reevaluated each step of the way. In order to reverse the biases already in artificial intelligence and prevent further biases from developing, companies must check their work over and over again.
Artificial intelligence can learn new data, and thus develop new biases as the system grows and changes. Companies must understand the impact that this could have on their systems and the people using them. Artificial intelligence software should be regularly evaluated, especially from the consumer-facing interface, so that biases are accounted for and eliminated.
Technology has never developed linearly. The same applies to artificial intelligence: Data, processes, systemsand even the bots themselves must be adjusted over time. The best avenue forward is to take a preventative approach. That means that these five steps to reduce bias in AI should be adjusted and repeated multiple times on any given system.
Human compassion is at the core of this mission toward equality in artificial intelligence. In order to create technology that serves humanity instead of harming it, we must build it with people in mind. It is a pivotal time in technology, and its essential that companies take a more human approach to artificial intelligence by focusing on creating systems free of bias.
Related:Watch Out for These 5Artificial IntelligenceProblems in HR
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5 Steps to Help Tech Companies Reduce Bias in AI - Entrepreneur
Artificial Intelligence (AI) in Contact Center Market Report : Industry Trends, Industry Share, Size, Growth and Opportunities 2025 | IBM, Google,…
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Artificial Intelligence (AI) in Contact Center Market Report : Industry Trends, Industry Share, Size, Growth and Opportunities 2025 | IBM, Google,...
UC adopts recommendations for the responsible use of Artificial Intelligence – Preuss School Ucsd
Camille Nebeker, Ed.D., associate professor with appointments in the UC San Diego Herbert Wertheim School of Public Health and Human Longevity Science and the Design Lab
The University of California Presidential Working Group on Artificial Intelligence was launched in 2020 by University of California President Michael V. Drake and former UC President Janet Napolitano to assist UC in determining a set of responsible principles to guide procurement, development, implementation, and monitoring of artificial intelligence (AI) in UC operations.
To support these goals, the working group developed a set of UC Responsible AI Principles and explored four high-risk application areas: health, human resources, policing, and student experience. The working group has published a final report that explores current and future applications of AI in these areas and provides recommendations for how to operationalize the UC Responsible AI Principles. The report concludes with overarching recommendations to help guide UCs strategy for determining whether and how to responsibly implement AI in its operations.
Camille Nebeker, Ed.D., associate professor with appointments in the UC San Diego Herbert Wertheim School of Public Health and Human Longevity Science and the Design Lab, was a member of the working groups health subcommittee.
The use of artificial intelligence within the UC campuses cuts across human resources, procurement, policing, student experience and healthcare. We, as an organization, did not have guiding principles to support responsible decision-making around AI, said Nebeker, who co-founded and directs the Research Center for Optimal Digital Ethics Health at UC San Diego, a multidisciplinary group that conducts research and provides education to support ethical digital health study practices.
The UC Presidential Working Group on AI has met over the past year to develop principles to advance responsible practices specific to the selection, implementation and management of AI systems.
With universities increasingly turning to AI-enabled tools to support greater efficiency and effectiveness, UC is setting an important precedent as one of the first universities, and the largest public university system, to develop governance processes for the responsible use of AI. More info is available on the UC Newsroom.
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UC adopts recommendations for the responsible use of Artificial Intelligence - Preuss School Ucsd