by BGF | Jan 13, 2019 | News
In 2018, Artificial Intelligence (AI) was mentioned almost every field. For the next year, there will be more incredible new discoveries and also the exaggeration of the commentators. There are five important things related to AI that are expected to happen in 2019:
1. AI will make extra contributions to international political issues
2018 has seen an increase in trade protectionism in the global trading system, especially in the relationship between top countries in the race for Artificial Intelligence, the US and China. While the US was imposing more rule of goods and services used to build AI, China was trying to explore on its own.
As a result, authoritarian regimes could assume the AI technology more and more to control freedoms. Moreover, the partnership between the AI organizations across the world could be compromised making the difficulties in setting common standards for AI.
2. Increasing the transparency of AI
Everyone should know what AI is and how it works to believe in AI. 2019 may see more measures created to increase the transparency of AI.
According to reports, many companies sometimes are repressing the setup of AI due to the fear of liabilities in the future if current technology were to be judged to be unfair or unethical in the future.
3. AI and robotization bearing further into each business
In financial services, immense ongoing logs of thousands of exchanges for each second are routinely parsed by machine learning calculations.
In 2019, what we’ll see developing certainty is that this keen, prescient innovation, supported by the learnings it has grabbed in its underlying organizations, can be taken off discount over the majority of a business’ activities.
AI will stretch out into help capacities, for example, HR or improving supply chains, where choices around coordination, just as procuring and terminating, will turn out to be progressively educated via computerization.
We’re additionally to see an expansion in organizations utilizing their information to create new income streams. In 2019 more organizations will receive this remarkably to comprehend the estimation of the data they possess.
4. A larger number of occupations will be made by AI than be lost to it.
As we referenced, in the long term the ascent of the machines will uncertainly prompt human joblessness and social struggle or something in the middle.
While 1.8 million occupations will be lost to computerization – 2.3 million will be made in assembling – one factor that can prompt this uniqueness is the accentuation on offering AI in the capacity to “strengthen” while conveying it in non-manual employment. This implies those ventures profit by the development in human occupations on the specialized side
For the monetary administrations, the viewpoint is maybe marginally grimmer. With back-office works progressively being managed via machines, we could be well on our approach to seeing that materialize by the end of next year.
5. AI collaborators will become really helpful
In 2019, more of the human than any time will utilize an AI associate to mastermind our timetables, plan our adventures, or request a pizza. These services will turn out to be progressively valuable as they figure out how to envision our practices better and comprehend our habits.
Information assembled from clients enables application designers to see precisely what the clients require. Therefore, capacities which we would like to utilize AI for –, for example, requesting taxicabs and sustenance deliveries– are ending up progressively streamlined and available.
Over this, AI associates are intended to end up progressively productive at understanding their human clients.
BGF is discussing these issues as well, including growing threats to democratic governance. We believe that AI technology and analytical thinking can help us face the challenges of nationalist populism and dictatorship.
by BGF | Jan 13, 2019 | News
According to a recent study, a new artificial intelligence (AI) technology can accurately identify rare genetic disorders using a photo of a patient’s face which could be of value in personalized medicine.
Named DeepGestalt, this new AI technology outperformed clinicians in identifying a range of syndromes in three trials. The study was published in the journal Nature Medicine.
According to the study, 8% of the population has diseases with key genetic components, and many have recognizable facial features. The DeepGestalt tool could identify, for example, Angelman syndrome with characteristic features such as a wide mouth or a protruding tongue.
Speaking about the technology, Yaron Gurovich, the chief technology officer at FDNA and lead researcher of the study, said: “It demonstrates how one can successfully apply state of the art algorithms, such as deep learning, to a challenging field where the available data is small, unbalanced in terms of available patients per condition, and where the need to support a large number of conditions is great.”
DeepGestalt was trained on 17,000 facial images of patients who had been diagnosed with over 200 distinct genetic maladies.
The deep learning algorithm outperformed clinicians in identifying a target syndrome among 502 chosen images, proposing a list of potential diseases and identifying the right one in its top 10 possibilities 91% of the time.
The AI also managed to outperform clinicians when it came to identifying subtypes in Noonan syndrome, achieving a 64% accuracy, compared to its human counterparts’ historical success rate of 20%.
Gurovich mentioned that one difficulty is the hard measure of the AI’s performance. He said: “The reason it is hard because there are not enough publicly available benchmarks.”
Jorge Cardoso, senior lecturer in artificial medical intelligence at the school of biomedical engineering and imaging sciences at King’s College London, expressed that the AI is “very interesting.”
“While several limitations still need to be addressed to ensure the proposed algorithms are robust in the hospital environment, clinically accurate, and applicable to different age groups and ethnic populations, the potential of AI in healthcare is immense,” said Cardoso.
Following the first layer of the AIWS 7-Layer Model—the set of ethical standards for AI developed by MDI—AI should not be able to put at risk the health and safety of humans. Therefore, we should always keep in mind the risk of malfunction in self-driving cars, and users’ safety needs to be guaranteed by developers.
by BGF | Jan 13, 2019 | News
Wikipedia is a profitable asset. But it’s not constantly clear how to order the substance on some random point into a coherent whole.
The Complete Guide is a profound tome. At in excess of 6,000 pages, this book is a complete prologue to machine learning, with up and coming sections on counterfeit neural systems, hereditary calculations, and machine vision.
However, this is no common production. It is a Wikibook, a reading material that anybody can get to or alter, made up from articles on Wikipedia, the immense online reference book.
Enter Shahar Admati and associates at the Ben-Gurion University of the Negev in Israel, these people have built up an approach to naturally create Wikibooks utilizing machine learning. They consider their machine the Wikibook-bot. “The oddity of our strategy is that it is gone for producing a whole Wikibook, without human association,” they state.
The specialists started by recognizing a lot of existing Wikibooks that can go about as a preparation informational index. They began with 6,700 Wikibooks, incorporated into an informational collection made accessible by Wikipedia, for this sort of scholarly examination.
Since these Wikibooks frame a sort of best quality level both for preparing and testing, the group required an approach to guarantee their quality. “We focused on Wikibooks that were seen something like multiple times, in view of the supposition that well known Wikibooks are of a sensible quality,” they state.
That left 490 Wikibooks that they separated further, in view of components, for example, having in excess of 10 sections. That left 407 Wikibooks that the group used to prepare their machines.
The group at that point separated the errand of making a Wikibook into a few sections, every one of which requires an alternate machine-learning aptitude. The errand starts with a title produced by a human, portraying an idea or something to that affect, for example, Machine Learning—The Complete Guide.
To help with this assignment, the group utilized the system structure of Wikipedia—articles regularly point to different articles utilizing hyperlinks. They started with the titles of the 407 Wikibooks made by people and played out the three-bounce examination. They at that point worked out the amount of the substance in the human-made books were incorporated by the computerized methodology.
The following stage is to compose the articles into parts. The last advance is to decide the request in which the articles ought to show up in every part. To do this, the group sort out the articles in sets and utilize a system based model to figure out which ought to seem first. By rehashing this for all mixes of article matches, the calculation works out a favored request for the articles and accordingly the sections. Along these ways, the group could deliver robotized adaptations of Wikibooks that had just been made by people.
It is intriguing work that can possibly deliver important course readings on a wide scope of points, and even to make different messages, for example, meeting procedures. Exactly how significant they will be to human perusers is yet to be resolved. Be that as it may, we will watch discover.
As AI develops at a very fast pace, it is necessary to observe its progress from time to time to keep it under control. Developers and organizations should use a certain set of standards to keep track of the technology’s development. The AIWS 7-layer model for AI ethical issues developed by MDI can be a good one to follow.
by BGF | Jan 13, 2019 | News
As a counter to the frenzy that AI will crush occupations, counseling firm KPMG today distributed a rundown of what it predicts will before long turn into the five most looked for after AI jobs. The expectations depend without anyone else ventures and those on which it exhorts. They are:
AI Architect – Responsible for working out where AI can encourage a business, estimating execution and—urgently—”continuing the AI show after some time.” Lack of designers “is the main motivation behind why organizations can’t effectively support AI activities,” KMPG notes.
AI Product Manager – Liaises between groups, ensuring thoughts can be actualized, particularly at scale. Works intimately with modelers, and with HR divisions to ensure people and machines would all be able to work successfully.
Data Scientist – Manages the tremendous measures of accessible information and plans calculations to make it important.
AI Technology Software Engineer – “One of the most concerning issues confronting organizations is getting AI from pilot stage to versatile sending,” KMPG composes. Programming engineers should be capable both to construct versatile innovation and see how AI really functions.
AI Ethicist – AI exhibits a large group of moral difficulties which will keep on unfurling as the innovation creates. Making rules and guaranteeing they’re maintained will progressively turn into an all-day work.
Preparing as AI is expanding “decently quickly” at colleges and different organizations, however not quickly enough, Brad Fisher (Brad Fisher, KPMG’s US lead on information and investigation) says, implying that request will keep on overwhelming supply for quite a while. While a few jobs could possibly be filled by existing workers—that of the ethicist, for instance—others require explicitly specialized know-how that may best be increased through outside preparing.
Somebody with “no specialized aptitudes presumably can’t be an information researcher, yet they may have the foundation to be an AI ethicist or even an AI venture chief,” he exhorts.
For the individuals who are as of now working however need to move into an AI job, Fisher says they ought to pick the sort of job that best suits their current abilities, and plan to “retool.”
With the purpose of ensuring AI’s future, the Michael Dukakis Institute has launched the AIWS Initiative, including the AIWS 7-Layer Model for ethical AI and concepts for the design of AI-Government, which has received the support of Paul Nemitz.
by BGF | Jan 13, 2019 | News, Practices in Cybersecurity
European elections have shown that the hunt of newspapers and public warnings in advance can help voters avoid campaigns that don’t bring information. But the battle with fake news may still be a cat-and-cat game between its suppliers and companies that have the platform they exploit.
Whether amateurs, criminals, or governments, many organizations – both domestic and foreign – have the skills to reverse the way the technology platform analyzes information.
Because of the huge amount of online information, people often feel overwhelmed and difficult to wonder what to focus on. Instead of information, incarnation and attention have become elusive. Big data and AI target micro into communication so that the information that people receive is limited to a sophisticated “filter bubble” of like-minded people.
People have proven that outrageous but misguided news will attract more viewers than accurate news. A study showed that such news on Twitter is likely to be 70% more forward than accurate news, and also create the most revenue. Actual tests with conventional media are often unable to keep up, and can sometimes be counterproductive when attracting more people to false information.
In essence, the ability of a profitable social media model to become a weapon of nations and non-national subjects is the same.
An arms race will continue between social media companies, states and non-governmental entities that have invested in the exploitation of their systems. Technology solutions such as artificial intelligence are not the key to solving all problems. Because these solutions are often sensational and outrageous, making fake news go further and faster than real news. Misinformation on Twitter is reused by many people and is much faster than real information, and repeating it even in the context of actual testing can increase the ability to accept fish information.
To prepare for the 2016 US presidential election, Internet Research Agency St. Petersburg, Russia, spent more than a year creating dozens of fake social media accounts like local US press agencies.
In any case, the damage caused by foreign actors may be less than the damage we cause to ourselves. The problem of “fake news” and foreign impersonation from reliable sources is difficult to resolve because it involves trade-offs between important values. Social media companies need to be vigilant when attacked to censor information for accuracy.
European elections have shown that the search for newspapers and public warnings in advance can help voters avoid campaigns that do not bring information.
But the fight with fake news may still be a cat-and-cat game between its suppliers and companies that have the platform they exploit. It will become part of the noisy elections everywhere. To protect our democracies, constant vigilance is indispensable.
In general, the current development of AI is not transparent enough to earn trust from people. With rules and orders, what the AIWS is working on, or ethical frameworks, which Michael Dukakis is building with AIWS Initiative, we can take a step closer to transparency and ethics in AI development.
by BGF | Jan 13, 2019 | News
The World Leadership Alliance – Club de Madrid (WLA – CdM) took part in Social and Emotional Learning: A Global Synthesis, which was held in Schloss Leopoldskron in Salzburg, Austria from 2-7 December 2018.
One of the first questions discussed at A Global Synthesis is the reason of rising for the need of social and emotional learning (SEL) skills around the globe.
The Seminar was organized in partnership with ETS, Microsoft and Qatar Foundation International, together with the British Council, the Calouste Gulbenkian Foundation and the Inter-American Development Bank. It will put the spotlight on what works and why in Social and Emotional Learning. 63 participants jointly examined advanced insights into SEL to build arguments for the requirement of SEL programs.
During the seminar, members reached an agreement to create a global collaborative network for social-emotional development, supports educator’s wellbeing and recommend universalizing SEL to schools and children.
Participants were divided into working groups to discuss concrete points to be integrated into the Final Statement
- Eight presentations from working groups are:
- Advocacy and Communication
- Assessment of SEL
- Community and SEL
- Educator Capacity Building
- Pedagogical Practice and Curriculum Integration
- SEL Global Alliance
- SEL in Crisis and Conflict Contexts
- SGS Working Group Outputs
The SEL platform will be a key education development to optimize student success and to improve educational attainment.
