Category Archives: Artificial Intelligence
Artificial Intelligence in India Opportunities, Risks …
Over the last two years, we have witnessed a steady increase in our percent of readership in India. Sometime in 2017, Bangalore became one of our largest sources of job applicants, and our single biggest city in terms of readers overtaking both London and NYC.
Given the Indian governments recent focus on developing a plan for artificial intelligence, we decided to apply our strengths (deep analysis of AI applications and implications) to determine (a) the state of AI innovation in India, and (b) strategic insights to help India survive and thrive in a global market with the help of AI initiatives.
We traveled to Bangalore in an effort to speak with experts from the Government of India, Indian AI startups, AI academic researchers in India and data science executives at some of the largest companies operating in India, including Reliance ADA, Amazon, AIG, Equifax, Infosys, NVIDIA and many more.
Through the course of this research our objective was threefold:
We have broken our analysis down into the following sections below:
Well begin by examining what we learned about AI adoption in India:
Since the early 90s, the IT and ITeS services sector in India has been of tremendous importance to its economy eventually growing to account for 7.7% of Indias GDP in 2016. In an attempt to capitalize on this foundation, the current Indian administration announced in February 2018 that the government think-tank, National Institution for Transforming India (NITI) Aayog (Hindi for Policy Commission), will spearhead a national programme on AI focusing on research.
This development comes on the heels of the launch of a Task Force on Artificial Intelligence for Indias Economic Transformation by the Commerce and Industry Department of the Government of India in 2017.
The industry experts we interviewed seemed to agree that artificial intelligence has certainly caught the attention of the Indian government and the tech community in recent years. According to Komal Sharma Talwar, Co-founder XLPAT Labs and member of Indias AI Task Force:
I think the government has realized that we need to have a formal policy in place so that theres a mission statement from them as to how AI should evolve in the country so its beneficial at large for the country.
Indeed its comments like Komals that made us realize that we should aid in determining a strategic direction for artificial intelligence development in India and learn as much as possible about the possible strategic value of the technology.
In our research and interviews, we saw consensus (from executives, non-profits, and researchers alike) that healthcare and agriculture would be among the most important sectors of focus in order to improve living conditions for Indias citizens.
Just as Google, Oracle, Microsoft, and Amazon are battling to serve the cloud computing and machine learning needs of the US government, the next three to five years may lead to a similar dynamic within India. As the Indian government pushes for digitization and enacts more AI initiatives, private firms will flock to win big contracts adding to the pool of funds to develop new technologies and spin out new AI and data science-related startups.
Mayank Kapur, CTO of Indian AI startup Gramener, says that the government is still the largest potential customer for data science services in the country. Other experts we spoke with have enunciated that more and more Indian startups and established tech firms are beginning to implement AI in their products and services.
Mr. Avik Sarkar, the Head of the Data Analytics Cell for NITI Aayog explains that the think-tank which has been tasked with spearheading Indias AI strategy is currently engaged in the following public sector initiatives:
The current areas of focus for AI applications in India are majorly focused in 3 areas:
With the governments growing interest around AI applications in India, Deepak Garg the Director at NVIDIA-Bennett Center of Research in Artificial Intelligence (andDirector LeadingIndia.ai) believes that there has been a significant growth in interest levels around AI across all industry sectors in India.
He explains that although AI attention is considerably smaller in India than in China or the USA, the increased AI interest has manifested itself in the following three ways:
1) Industries have started working to skill their manpower to enable themselves to compete with other global players
2) Educational institutions have started working on their curricula to include courses on machine learning and other relevant areas
3) Individuals and professionals have started acquiring these skills and are comfortable investing in upgrading their own skills.
Despite the initial enthusiasm for AI, there were also a few opinions from experts about a sense of unfulfilled potential and that the country could be doing far more to adopt and integrate AI technologies.
Another common theme we heard often during our interviews was that culturally speaking the cost of failure is much higher in India than the West. While failing in an attempt at bold innovation and grand goals might be seen as noble or brave in Silicon Valley or New York City (or even Boston), failure often implies a loss of face in India and some Asian countries. This has historically meant a lack of room for innovative experimentation.
Dr. Nishant Chandra, the Data Science Leader of Science group at AIG adds a valuable insight about the high stakes for failure in India and that cultural and economic factors play into raising these stakes:
Indian society is not as forgiving to failure in entrepreneurship as US or Europe. So far, this has led to ideas borrowed from other places and implemented after customization. Yet I believe, entrepreneurs will build upon the success of IT services industry and establish globally competitive AI companies in near future.
We caught up with Professor Manish Gupta at IIIT Bangalore Manish is also a startup founder (VideoKen) and former AI researcher at Xerox and Goldman Sachs India. He expressed his disappointment in Indias lack of global AI participation:
I think that we are not doing enough justice to our potential [in India]; I think we are really far behind some of the other leaders. I see a lot of American and Chinese companies at global AI conference like NIPS / AAAI and these two countries seem to be far ahead of the rest of the pack. I look at India as a country that ought to be doing a lot more.
A number of our interviewees mentioned the prevalence of copy-catting business models in India (taking a famous or successful business model in the USA or Europe and reconstructing it in India), as opposed to the invention of entirely new business models.
Google is not the copy-cat of another business in another country, nor is Facebook, Amazon, or Microsoft and many of the same interviewees we spoke with are hopeful that India will have its own global trend-setters as its technology ecosystem develops.
Our previous research on AI enterprise adoption seems to indicate that it may be another 2-5 years until AI adoption becomes mainstream in the Fortune 500 and even that is only at the level of pilots and initiatives, not of revolutionary results.
This learning phase evident given the state of AI adoption the Western markets may last longer in Indias relatively underdeveloped economy.
Aakrit Vaish, CEO of Haptik, Inc. also seems to suggest that in the next 10 years we can expect that understanding of AI and how it works will potentially be more commonplace among most technical industry executives:
India may go in the direction that China has gone, become their own economies. There are probably going to be pockets, Bangalore might be good at deep tech like robotics or research / Hyderabad being good at data/ AI training, Mumbai being good at BFSI and Delhi for agriculture and government. Like China, most solutions will probably be applied to the local economy.
Indias services sector (call centers, BPOs, etc roughly 18% of the Indian GDP) have a significant potential opportunity to cater to the coming demand for data cleaning and human-augmented AI training (data labeling, search engine training, content moderation, etc).
Komal Talwar from Government of Indias AI Task Force added her views on what the Indian governments future strategy around AI might be focused on:
We think AI could have a great impact in health sector. There is a scarcity for good doctors and nurses, with AI the machine can do the first round of diagnostics. Staff can carry machines with them to help cut down in the physical presence needed for doctors.
The government is really encouraging startups to have AI applications that really have a social impact (AI in health, AI in education, etc), where startups compete to solve social problems.
Has India woken up to artificial intelligence? Expert opinions on this topic seem mixed, yet through our analysis, we managed to distill the following themes:
Interested readers can learn more about AI applications in India today from our other articles about AI traction in some of Indias largest sectors:
The majority of our Indian AI respondents and interviewees showed optimism about Indias potential to be one of the key global players in the future of AI. Optimism about the prospects of ones own nations success seems a natural bias (and one that weve seen before in our geography-specific coverage in Montreal, Boston, and more) but Indias optimism isnt unwarranted.
Since the early 90s when the Indian economy opened up to foreign investment, the country has been considered by some economists as the dark horse among the larger economies in the world.
Historically, the slower adoption of IT services by domestic Indian companies (in some cases by even by a period of around 10 years) as compared to global competitors was an indicator of the unfulfilled potential according to some experts we spoke to.
Yet, most of the interviewees seemed bullish on the fact that this time around in the wave of AI, India is firmly backing its strengths as represented in the quote below from Aakrit Vaish Co-founder and CEO of Haptik, Inc.
The Indian foundation of IT services and business process outsourcing makes me believe that such AI training jobs will be even more lucrative for India than elsewhere in the future.
During the interview with him, Aakrit explained his stance with an example about the possibility that Indian BPO services providers could potentially be attractive in terms of skills and cost for tasks (which he believes will for a long time remain a manual effort) like cleaning and tagging of data in the near future.
We heard opinions from other experts favoring the view that India may be positioned well to take advantage of the AI disruption. Sundara Ramalingam Nagalingam, Head of Deep Learning Practice at NVIDIA India, shares his thoughts on some of the advantages India may have over other countries in terms of AI:
India is the third largest startup ecosystem in the world, with three to four startups being born here daily. We believe India has a major advantage over other countries in terms of talent, a vibrant startup ecosystem, strong IT services and an offshoring industry to harness the power of AI.
Kiran Rama, the Director of Data Sciences at the VMware Center of Excellence (CoE) in Bangalore also seems to agree that the cost-competitive talent in India will be an opportunity for companies looking to open offices in India:
There seems to be a lot of opportunity for companies that are setting u shop in India. Especially since there is a supply of data science talent at a good cost advantage. I also think there Indians are starting to contribute to the advancement of machine learning libraries and algorithms.
Subramanian Mani, who heads the analytics wing at BigBasket.com, an online Indian grocery e-commerce firm, reiterates the idea that the IT services background in India is an advantage.
He believes that the major difference between the software and AI waves is that although India was slow to adopt software service as compared to America, this time around with the AI wave, adoption will be much faster and only slightly behind the leading countries.
This is the second wave. The software wave was 30 years ago. Folks in India realized that theyve been able to scale software and I think AI / ML is an extension of software development.
While software was often taught through books and in classrooms exclusively, many of the latest artificial intelligence approaches are available to learn online along with huge suites of open-source tools (from scikit-learn to TensorFlow and beyond).
Going in, we knew that one of the key advantages for India would, in fact, be the very IT and ITeS sectors which will make it easy for Indian tech providers to transition into AI services, given that well-developed ecosystems have evolved over the past 25 years in cities like Bangalore and Hyderabad.
Manish Gupta, Director of Machine Learning & Data Science at American Express India, expressed optimism in Bangalore as an innovation hub:
Bangalore has always been seen as the Silicon Valley of India and today there are lots of analytics companies here. It has all the ingredients to be a leader in the AI space. The state government is interested in planning and grooming for startups in this space as witnessed by the launch of the Center for Excellence (CoE) in AI setup by the GOI and NASSCOM in Bangalore.
While the advantage from the existing Indian IT sector may have been more intuitive, Madhusudan Shekar, Principal Technology Evangelist at Amazon AWS explains through an example how Indias diversity and scale (generally considered a challenge) can be an opportunity to make the best out of a tough situation:
In India, people speak over 40+ formal languages in about 800+ dialects. There are 22 national languages and if you want to build a neural network for speech, India is the best place to build that neural net. If you can build for India, you can most likely build it for other parts of the world.
In this respect, India with all of its language challenges could be a petri dish for translation-oriented AI applications. The market for this technology especially when backed by the Indian government may well rival the kind of AI innovations developed around translation in other parts of the world.
Another insight that was oft repeated by the experts was around the potential to have access to vast amounts of data in India. To further explain, According to a report by the Telecom Regulatory Authority of India (TRAI) the total number of internet subscribers in the country as a percentage of the overall population increased by 12.01% from December 2013 to reach 267.39 million in December 2014.
Along these lines, Mayank Kapur Co-founder of Gramener cites the increased level of data collection and the scale to which it could potentially grow as an opportunity for India in public sector AI applications:
In the public sector, we have an advantage of scale the amount of data that can potentially be gathered is huge. For example, leveraging data to provide access to services is a huge differentiator in the healthcare sector for applications like disease prevention or nutrition.
Figure. Number of internet subscribers
in India in 2014 by access type (Source)
Juergen Hase the CEO of Unlimit- A Reliance Group Company, one of Indias largest private sector companies, expressed his thoughts during our research:
The direct switch to mobile platforms in India means that there are no legacy systems to deal with and new technologies can be developed from scratch.
As shown in the figure to the right, an overwhelming majority of Indias Internet subscribers gain access through mobile wireless networks.
As Juergen points out, what this means is that large-scale AI projects in India can be somewhat insulated from issues cropping up from legacy systems. This might also lead to a greater immediate mobile-fluency for Indias startup and developer communities, who need to appeal to an almost exclusively mobile market.
Juergen adds, in the future, we can expect that AI software will also potentially have this advantage in India as compared to developed countries where the ratio is more evenly distributed among mobile and fixed wireless users.
We think our business audience will indeed find the next quote from Avi Patchava, Vice President, Data Sciences, ML & AI InMobi, highly insightful in terms of gaining an overview of Indias biggest strengths with respect to the countrys ability to leverage AI. Avi neatly summed up what he believes are Indias four biggest strengths to face the upcoming AI disruption:
The following points became evident through our interviews about Indias AI strengths and opportunities:
While there were many favorable views on the future outlook of the Indian AI ecosystem, there seemed to be different views among experts regarding the challenges that the country might have to overcome to survive and thrive in the AI disruption.
We heard a significant number of experts allude to the fact that the hype around AI may still be very real in India and there exists here a common tendency to view AI as a discrete industry rather than the broad, core technology that it is (like the internet).
In addition to being misunderstood and not being properly leveraged, many of the experts we spoke with were candid about addressing what they see as relative weaknesses of the Indian AI ecosystem.
Aakrit Vaish from Haptik, Inc. shares his thoughts on the AI hype that he sees in the Indian tech scene today:
Today AI is getting a lot of attention in India but nobody knows what it is or what are the best applications for it. Theres a little of a spray-and-pray attitude across the board.
While AI hype is hard to escape in the tech press in any country our speaking engagements in India seemed to affirm the state ambiguity around AI. We received post-presentation questions from attendees (about AI taking jobs, about the definition of AI itself, about the ongoings of Google and Facebook) that seemed like less informed questions than we might hear from a similarly technical audience in Boston or San Francisco.
This may mostly be due to the fact that AI applications are less well understood, and genuinely knowledgeable AI talent is rarer. We might suspect that over the coming few years particularly in a tech hub like Bangalore wed see this knowledge lessen over time.
Co-founder of XLPAT Labs and member of Indias AI Task Force Komal Sharma specifically points out that even some of the government projects have faced issues in terms of receiving funding for initiating AI pilot projects. She seems to indicate that the current Indian AI and startup funding ecosystem is not mature enough to be comparable to the US or even China.
The problem that we have faced I think is funding in areas where our field is very niche. In India, IP is developing lots of interest, but were nowhere near the US or other countries.
Komal was far from being alone in her lamenting AIs lack of VC funding, and the sentiment of our respondents seems to be backed up by the data.
The World Economic Forum chart below features information from Ernst & Young:
Taken as a percent of GDP, Israels VC investments represent about 0.006% of GDP, while Indias investments represent around 0.002%. As the Indian economy continues to develop and if Indias entrepreneurship trend continues we should expect to see investment increase.
Madhu Gopinathan Vice President, Data Science at MakeMyTrip,Indias largest online travel company,touches on a point repeated by other experts as well. He thinks that the two underlying factors here are larger salaries lie in the corporate sector, which is potentially creating a dearth of mentors for the next generation of software developers looking to transition into AI and the availability of data.Academia and Industry collaboration is a serious issue in India. Although we have a lot of universities, the incentives are skewed towards the corporate sector. For example, people like me who have an understanding of the technology may not be inclined to teach the next generation at universities, since working at the larger companies is far more lucrative today.
Madhu believes that much of the AI upskilling of Indias development talent will occur on the job in the cutting-edge work environments of venture-backed companies, as opposed to in the classroom.
As Nishant Chandra from AIG puts it, the boom in the Indian IT services sector in the early 90s was partially born out of necessity India just did not have a good products ecosystem. India has historically not done well with products and according to the experts, there also seems to be a dearth of good talent specifically for design and user-interface functions.
Sumit Borar, Sr. Director Data Sciences at Myntra, the Indian fashion eCommerce firm, is of the opinion that the scale of AI talent in India is still very nascent although he expects this to change in the next three years:
Talent will be the biggest strength for India with respect to AI. But AI is still new, so current talent in the market is very limited but in 3 years time I think that will become a strength.
Industry-university partnerships where students can work with real world data science applications and reskilling of existing workforces (example: getting software engineers to look at statistics or vice versa) are just beginning to take shape in India (starting with the unicorns).
The cultural factors in India play a role in talent development here as explained by Nimilita Chatterjee SVP, Data and Analytics at Equifax:
I see issues in AI talent in India are at 3 levels:
The issues that Nimilita addresses above arent all that different from what we see in the United States (indeed in Silicon Valley) on a daily basis. It does seem safe to say, however, that experienced data science talent (more specifically: Talent who have applied data science and AI skills in a real business context) is much more sparse in India than it is in the USA at least for now.
Nilmilita also believes that another weakness for India today in terms of data access for AI applications in the finance sector stems from the fact that the Indian economy still operates primarily on cash. As of 2017, Indias Economic Times claims that cash comprises 95% of the Indian economy.
Although there is a small percentage of the population that is making the switch to digital transactions, she believes that this segment of the population is still not significant enough before AI adoption in this sector becomes widespread in India.
India moving away from cash and being comfortable on a mobile phone, however that part of the population is still small. It will come into play in the future, but today it is still an issue in the finance sector.
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Artificial Intelligence in India Opportunities, Risks ...
The Global Artificial Intelligence in Aviation Market is …
The positioning of the Global Artificial Intelligence in Aviation Market vendors in FPNV Positioning Matrix are determined by Business Strategy (Business Growth, Industry Coverage, Financial Viability, and Channel Support) and Product Satisfaction (Value for Money, Ease of Use, Product Features, and Customer Support) and placed into four quadrants (F: Forefront, P: Pathfinders, N: Niche, and V: Vital).
New York, March 28, 2020 (GLOBE NEWSWIRE) -- Reportlinker.com announces the release of the report "Global Artificial Intelligence in Aviation Market - Premium Insight, Competitive News Feed Analysis, Company Usability Profiles, Market Sizing & Forecasts to 2025" - https://www.reportlinker.com/p05871978/?utm_source=GNW
The report deeply explores the recent significant developments by the leading vendors and innovation profiles in the Global Artificial Intelligence in Aviation Market including are Intel, Micron, Nvidia, Samsung Electronics, Xilinx, Airbus, Amazon, Boeing, Garmin, GE, IBM, Lockheed Martin, Microsoft, and Thales.
On the basis of Technology, the Global Artificial Intelligence in Aviation Market is studied across Computer Vision, Context Awareness Computing, Machine Learning, and Natural Language Processing (Nlp).
On the basis of Offering, the Global Artificial Intelligence in Aviation Market is studied across Hardware, Services, and Software.
On the basis of Application, the Global Artificial Intelligence in Aviation Market is studied across Dynamic Pricing, Flight Operations, Manufacturing, Smart Maintenance, Surveillance, Training, and Virtual Assistants.
For the detailed coverage of the study, the market has been geographically divided into the Americas, Asia-Pacific, and Europe, Middle East & Africa. The report provides details of qualitative and quantitative insights about the major countries in the region and taps the major regional developments in detail.
In the report, we have covered two proprietary models, the FPNV Positioning Matrix and Competitive Strategic Window. The FPNV Positioning Matrix analyses the competitive market place for the players in terms of product satisfaction and business strategy they adopt to sustain in the market. The Competitive Strategic Window analyses the competitive landscape in terms of markets, applications, and geographies. The Competitive Strategic Window helps the vendor define an alignment or fit between their capabilities and opportunities for future growth prospects. During a forecast period, it defines the optimal or favorable fit for the vendors to adopt successive merger and acquisitions strategies, geography expansion, research & development, new product introduction strategies to execute further business expansion and growth.
Research Methodology:Our market forecasting is based on a market model derived from market connectivity, dynamics, and identified influential factors around which assumptions about the market are made. These assumptions are enlightened by fact-bases, put by primary and secondary research instruments, regressive analysis and an extensive connect with industry people. Market forecasting derived from in-depth understanding attained from future market spending patterns provides quantified insight to support your decision-making process. The interview is recorded, and the information gathered in put on the drawing board with the information collected through secondary research.
The report provides insights on the following pointers:1. Market Penetration: Provides comprehensive information on sulfuric acid offered by the key players in the Global Artificial Intelligence in Aviation Market 2. Product Development & Innovation: Provides intelligent insights on future technologies, R&D activities, and new product developments in the Global Artificial Intelligence in Aviation Market 3. Market Development: Provides in-depth information about lucrative emerging markets and analyzes the markets for the Global Artificial Intelligence in Aviation Market 4. Market Diversification: Provides detailed information about new products launches, untapped geographies, recent developments, and investments in the Global Artificial Intelligence in Aviation Market 5. Competitive Assessment & Intelligence: Provides an exhaustive assessment of market shares, strategies, products, and manufacturing capabilities of the leading players in the Global Artificial Intelligence in Aviation Market
The report answers questions such as:1. What is the market size of Artificial Intelligence in Aviation market in the Global?2. What are the factors that affect the growth in the Global Artificial Intelligence in Aviation Market over the forecast period?3. What is the competitive position in the Global Artificial Intelligence in Aviation Market?4. Which are the best product areas to be invested in over the forecast period in the Global Artificial Intelligence in Aviation Market?5. What are the opportunities in the Global Artificial Intelligence in Aviation Market?6. What are the modes of entering the Global Artificial Intelligence in Aviation Market?Read the full report: https://www.reportlinker.com/p05871978/?utm_source=GNW
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The Global Artificial Intelligence in Aviation Market is ...
Artificial Intelligence in Business: How to Use AI in Your …
Artificial intelligence (AI) in business is rapidly becoming a commonly-used competitive tool. Clearly, companies are past debating the pros and cons of AI. From better chatbots for customer service to data analytics to making predictive recommendations, deep learning and artificial intelligence in their many forms is seen by business leaders as an essential tool.
That puts AI in the short-list of technologies that your company should not just be watching, but actively exploring how to take advantage of. It joins leading emerging technologies like Machine Learning, cloud computing and Big Data.
If you aren't convinced that AI is ready to handle a growing number and range of tasks, consider IBM's Watson's 2011 winning performance on Jeopardy. Or consider the various ways you are likely already using AI-enabled devices and services in your personal life, like smart assistant apps or devices like Amazon's Alexa or Apple's Siri. Not to mention other AI-supercharged apps, such as whatever GPS app you use while driving.
Here's a quick look at how your competitors are already using AI in their business, and some advice on how to get on board.
Jump to:
Results of a recent survey indicate that artificial intelligence can assist businesses in areas ranging from customer support to personalization.
Odds are you can't just call up your competitors and ask how they are using AI in their company. But thanks to the Internet, you can find out a lot of what they have said. For example, web-searching "how is Staples using AI" yields informative results from about how that company is putting artificial intelligence technology to work for itself.
Next, check your competitors' web sites and social media presences (notably LinkedIn and Facebook). Browse their press releases, news coverage, and blogs. You might even go old-school, and get any hardcopy newsletters, annual reports or other literature from the past year that might not be available online.
Then cast a wider net, with an industry search, like "how are hospitals using AI," "how are grocery stores using AI," or even a more general search.
For example, when I did a search for "using AI in my business," I got various hits talking about business uses for AI including:
Another suggestion: research how other parts of your supply chain parts, shipping, support, and the like are using AI.
Don't forget other IRL (In Real Life)/non-digital avenues. If you are going to an industry event, look for AI-related sessions. Chat up whoever you're standing or sitting near. And, of course, you could always read a book or two although, by definition, that advice will be at least six to twelve months out of date.
If you know anybody at one or more competitors who's informed and amenable, buy them a lunch and pick their brain.
Businesses have high hopes that AI can help them predict a wide array of activities.
Based on your research, you should be able to build a list and frame a sense of what AI can do for businesses in general, and for companies in your industry and of your size.
You'll find several major key areas:
Based on this list, your next step is to come up with a short list of how artificial intelligence can help your business specific tasks and use cases.
To help make this list:
Then, prioritize that list based on a mix of estimated costs, time to implement, risk/benefit, and overall value.
In parallel, select one or two smaller tasks for trying artificial intelligence for your business. This could be a small piece of a larger task. Important: start with a task that is not business-critical. Another quick tip: Start with tasks that aren't customer-facing.
Now it's time to identify potential technology vendors. There is no shortage of top artificial intelligence companies.
In order to find and compare vendors, you first have to assess how you might add artificial intelligence capabilities to your company's IT, which in turn depends on factors like:
Vendors for AI capabilities spans several categories:
For some of the AI you're looking for, your current vendors may already offer. In other cases, you may outsource. For still others, you may end up doing internally. It all depends on what you want, how much developer bandwidth you have in-house, and how you provision your IT operations.
Your best bet will be to find one or more AI experts, either internally, or outside consultants. For the latter, start with ones who aren't part of a vendor... unless the vendor is offering AI that is a match for your criteria.
Once you have identified your initial AI projects the real fun begins: implementation. Essential milestones:
Key: Be ready to revisit constantly.
Just because you have AI projects out of development and testing, and contributing to your business, that doesn't mean you're done. Just as provisioning infrastructure or updating your company's web and social presence is never done.
In addition to tracking your selected AI vendors for improvements, new features you want to stay on top of other AI developments. For example, what new capabilities have become available? What improvements in infrastructure performance or price make existing or new AI offerings now viable?
And, of course, you want to keep up with what others in your industry, and the AI vendors serving your industry, are doing or have on their road map.
Adding AI your company's operations and business is a big change, and likely a big transformation. Here's some quick advice to lessen the challenges:
Plus, focus on AI that's available as a supported product/service, rather than something still in development.
Although AI as an area within computer science dates back to the 1950's, it's only been within the past decade that many types of AI have become available to companies of all sizes.
This is thanks to factors like continuing hardware price/performance improvements, cloud computing, and advances in AI techniques. At the same time, computing trends like big data, IoT, self-driving vehicles, and speech and image recognition are generating more "targets" to point AI tools at.
In particular, Keep an eye on cloud costs and capabilities, along with what the various players are doing or talking about, AI-wise. Like nearly everything involving computer technology, many of the next cool capabilities can't be anticipated or predicted. Bottom line: talk to professionals in your field nothing will help you quite as much.
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Artificial Intelligence in Business: How to Use AI in Your ...
10 Business Functions That Are Ready To Use Artificial Intelligence – Forbes
In the grand scheme of things, artificial intelligence (AI) is still in the very early stages of adoption by most organizations. However, most leaders are quite excited to implement AI into the companys business functions to start realizing its extraordinary benefits. While we have no way of knowing all the ways artificial intelligence and machine learning will ultimately impact business functions, here are 10 business functions that are ready to use artificial intelligence.
10 Business Functions That Are Ready To Use Artificial Intelligence
Marketing
If your company isnt using artificial intelligence in marketing, it's already behind. Not only can AI help to develop marketing strategies, but it's also instrumental in executing them. Already AI sorts customers according to interest or demographic, can target ads to them based on browsing history, powers recommendation engines, and is a critical tool to give customers what they want exactly when they want it. Another way AI is used in marketing is through chatbots. These bots can help solve problems, suggest products or services, and support sales. Artificial intelligence also supports marketers by analyzing data on consumer behavior faster and more accurately than humans. These insights can help businesses make adjustments to marketing campaigns to make them more effective or plan better for the future.
Sales
There is definitely a side of selling products and services that is uniquely human, but artificial intelligence can arm sales professionals with insights that can improve the sales function. AI helps improve sales forecasting, predict customer needs, and improve communication. And intelligent machines can help sales professionals manage their time and identify who they need to follow-up with and when as well as what customers might be ready to convert.
Research and Development (R&D)
What about artificial intelligence as a tool of innovation? It can help us build a deeper understanding in nearly any industry, including healthcare and pharmaceuticals, financial, automotive, and more, while collecting and analyzing tremendous amounts of information efficiently and accurately. This and machine learning can help us research problems and develop solutions that weve never thought of before. AI can automate many tasks, but it will also open the door to novel discoveries, ways of improving products and services as well as accomplishing tasks. Artificial intelligence helps R&D activities be more strategic and effective.
IT Operations
Also called AIOps, AI for IT operations is often the first experience many organizations have with implementing artificial intelligence internally. Gartner defines the term AIOps as the application of machine learning and data science to IT operations problems. AI is commonly used for IT system log file error analysis, with IT systems management functions as well as to automate many routine processes. It can help identify issues so the IT team can proactively fix them before any IT systems go down. As the IT systems to support our businesses become more complex, AIOps helps the IT improve system performance and services.
Human Resources
In a business function with human in the name, is there a place for machines? Yes! Artificial intelligence really has the potential to transform many human resources activities from recruitment to talent management. AI can certainly help improve efficiency and save money by automating repetitive tasks, but it can do much more. PepsiCo used a robot, Robot Vera, to phone and interview candidates for open sales positions. Talent is going to expect a personalized experience from their employer just as they have been accustomed to when shopping and for their entertainment. Machine learning and AI solutions can help provide that. In addition, AI can help human resources departments with data-based decision-making and make candidate screening and the recruitment process easier. Chatbots can also be used to answer many common questions about company policies and benefits.
Contact Centers
The contact center of an organization is another business area where artificial intelligence is already in use. Organizations that use AI technology to enhance rather than replace humans with these tasks are the ones that are incorporating artificial intelligence in the right way. These centers collect a tremendous amount of data that can be used to learn more about customers, predict customer intent, and improve the "next best action" for the customer for better customer engagement. The unstructured data collected from contact centers can also be analyzed by machine learning to uncover customer trends and then improve products and services.
Building Maintenance
Another way AI is already at work in businesses today is helping facilities managers optimize energy use and the comfort of occupants. Building automation, the use of artificial intelligence to help manage buildings and control lighting and heating/cooling systems, uses internet-of-things devices and sensors as well as computer vision to monitor buildings. Based upon the data that is collected, the AI system can adjust the building's systems to accommodate for the number of occupants, time of day, and more. AI helps facilities managers improve energy efficiency of the building. An additional component of many of these systems is building security as well.
Manufacturing
Heineken, along with many other companies, uses data analytics at every stage of the manufacturing process from the supply chain to tracking inventory on store shelves. Predictive intelligence can not only anticipate demand and ramp production up or down, but sensors on equipment can predict maintenance needs. AI helps flag areas of concern in the manufacturing process before costly issues erupt. Machine vision can also support the quality control process at manufacturing facilities.
Accounting and Finance
Many organizations are finding the promise of cost reductions and more efficient operations the major appeal for artificial intelligence in the workplace, and according to Accenture Consulting, robotic process automation can produce amazing results in these areas for the accounting and finance industry and departments. Human finance professionals will be freed-up from repetitive tasks to be able to focus on higher-level activities while the use of AI in accounting will reduce errors. AI is also able to provide real-time status of financial matters to organizations because it can monitor communication through natural language processing.
Customer Experience
Another way artificial intelligence technology and big data are used in business today is to improve the customer experience. Luxury fashion brand Burberry uses big data and AI to enhance sales and customer relationships. The company gathers shopper's data through loyalty and reward programs that they then use to offer tailored recommendations whether customers are shopping online or in brick-and-mortar stores. Innovative uses of chatbots during industry events are another way to provide a stellar customer experience.
For more on AI and technology trends, see Bernard Marrs bookArtificial Intelligence in Practice: How 50 Companies Used AI and Machine Learning To Solve Problemsand his forthcoming bookTech Trends in Practice: The 25 Technologies That Are Driving The 4ThIndustrial Revolution, which is available to pre-order now.
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10 Business Functions That Are Ready To Use Artificial Intelligence - Forbes
COVID-19: AI can help – but the right human input is key – World Economic Forum
Artificial intelligence (AI) has the potential to help us tackle the pressing issues raised by the COVID-19 pandemic. It is not the technology itself, though, that will make the difference but rather the knowledge and creativity of the humans who use it.
Indeed, the COVID-19 crisis will likely expose some of the key shortfalls of AI. Machine learning, the current form of AI, works by identifying patterns in historical training data. When used wisely, AI has the potential to exceed humans not only through speed but also by detecting patterns in that training data that humans have overlooked.
However, AI systems need a lot of data, with relevant examples in that data, in order to find these patterns. Machine learning also implicitly assumes that conditions today are the same as the conditions represented in the training data. In other words, AI systems implicitly assume that what has worked in the past will still work in the future.
A new strain of Coronavirus, COVID 19, is spreading around the world, causing deaths and major disruption to the global economy.
Responding to this crisis requires global cooperation among governments, international organizations and the business community, which is at the centre of the World Economic Forums mission as the International Organization for Public-Private Cooperation.
The Forum has created the COVID Action Platform, a global platform to convene the business community for collective action, protect peoples livelihoods and facilitate business continuity, and mobilize support for the COVID-19 response. The platform is created with the support of the World Health Organization and is open to all businesses and industry groups, as well as other stakeholders, aiming to integrate and inform joint action.
As an organization, the Forum has a track record of supporting efforts to contain epidemics. In 2017, at our Annual Meeting, the Coalition for Epidemic Preparedness Innovations (CEPI) was launched bringing together experts from government, business, health, academia and civil society to accelerate the development of vaccines. CEPI is currently supporting the race to develop a vaccine against this strand of the coronavirus.
What does this have to do with the current crisis? We are facing unprecedented times. Our situation is jarringly different from that of just a few weeks ago. Some of what we need to try today will have never been tried before. Similarly, what has worked in the past may very well not work today.
Humans are not that different from AI in these limitations, which partly explains why our current situation is so daunting. Without previous examples to draw on, we cannot know for sure the best course of action. Our traditional assumptions about cause and effect may no longer hold true.
Humans have an advantage over AI, though. We are able to learn lessons from one setting and apply them to novel situations, drawing on our abstract knowledge to make best guesses on what might work or what might happen. AI systems, in contrast, have to learn from scratch whenever the setting or task changes even slightly.
The COVID-19 crisis, therefore, will highlight something that has always been true about AI: it is a tool, and the value of its use in any situation is determined by the humans who design it and use it. In the current crisis, human action and innovation will be particularly critical in leveraging the power of what AI can do.
One approach to the novel situation problem is to gather new training data under current conditions. For both human decision-makers and AI systems alike, each new piece of information about our current situation is particularly valuable in informing our decisions going forward. The more effective we are at sharing information, the more quickly our situation is no longer novel and we can begin to see a path forward.
Projects such as the COVID-19 Open Research Dataset, which provides the text of over 24,000 research papers, the COVID-net open-access neural network, which is working to collaboratively develop a system to identify COVID-19 in lung scans, and an initiative asking individuals to donate their anonymized data, represent important efforts by humans to pool data so that AI systems can then sift through this information to identify patterns.
Global spread of COVID-19
Image: World Economic Forum
A second approach is to use human knowledge and creativity to undertake the abstraction that the AI systems cannot do. Humans can discern between places where algorithms are likely to fail and situations in which historical training data is likely still relevant to address critical and timely issues, at least until more current data becomes available.
Such systems might include algorithms that predict the spread of the virus using data from previous pandemics or tools that help job seekers identify opportunities that match their skillsets. Even though the particular nature of COVID-19 is unique and many of the fundamental rules of the labour market are not operating, it is still possible to identify valuable, although perhaps carefully circumscribed, avenues for applying AI tools.
Efforts to leverage AI tools in the time of COVID-19 will be most effective when they involve the input and collaboration of humans in several different roles. The data scientists who code AI systems play an important role because they know what AI can do and, just as importantly, what it cant. We also need domain experts who understand the nature of the problem and can identify where past training data might still be relevant today. Finally, we need out-of-the-box thinkers who push us to move beyond our assumptions and can see surprising connections.
Toronto-based startup Bluedot is an example of such a collaboration. In December it was one of the first to identify the emergence of a new outbreak in China. Its system relies on the vision of its founder, who believed that predicting outbreaks was possible, and combines the power several different AI tools with the knowledge of epidemiologists who identified where and how to look for evidence of emerging diseases. These epidemiologists also verify the results at the end.
Reinventing the rules is different from breaking the rules, though. As we work to address our current needs, we must also keep our eye on the long-term consequences. All of the humans involved in developing AI systems need to maintain ethical standards and consider possible unintended consequences of the technologies they create. While our current crisis is very pressing, we cannot sacrifice our fundamental principles to address it.
The key takeaway is this: Despite the hype, there are many ways that humans in which still surpass the capabilities of AI. The stunning advances that AI has made in recent years are not an inherent quality of the technology, but rather a testament to the humans who have been incredibly creative in how they use a tool that is mathematically and computationally complex and yet at its foundation still quite simple and limited.
As we seek to move rapidly to address our current problems, therefore, we need to continue to draw on this human creativity from all corners, not just the technology experts but also those with knowledge of the settings, as well as those who challenge our assumptions and see new connections. It is this human collaboration that will enable AI to be the powerful tool for good that it has the potential to be.
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Written by
Matissa Hollister, Assistant Professor of Organizational Behaviour, McGill University
The views expressed in this article are those of the author alone and not the World Economic Forum.
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COVID-19: AI can help - but the right human input is key - World Economic Forum
6 Visions of How Artificial Intelligence will Change Architecture – ArchDaily
6 Visions of How Artificial Intelligence will Change Architecture
Or
In his book "Life 3.0", MIT professor Max Tegmark says "we are all the guardians of the future of life now as we shape the age of AI." Artificial Intelligence remains a Pandora's Box of possibilities, with the potential to enhance the safety, efficiency, and sustainability of cities, or destroy the potential for humans to work, interact, and live a private life. The question of how Artificial Intelligence will impact the cities of the future has also captured the imagination of architects and designers, and formed a central question to the 2019 Shenzhen Biennale, the world's most visited architecture event.
As part of the "Eyes of the City" section of the Biennial, curated by Carlo Ratti, designers were asked to put forth their visions and concerns of how artificial intelligence will impact the future of architecture. Below, we have selected six visions, where designers reflect in their own words on aspects from ecology and the environment to social isolation. For further reading on AI and the Shenzhen Biennial, see our interview with Carlo Ratti and Winy Maas on the subject, and visit our dedicated landing page of content here.
The advance of AI technologies can make it feel as if we know everything about our citiesas if all city dwellers are counted and accounted for, our urban existence fully monitored, mapped, and predicted.
But what happens when we train our attention and technologies on the non-human beings with whom we share our urban environments? How can our notion of urban life, and the possibilities to design for it, expand when we use technology to visualize more than just the relationship between humans and human-made structures?
There is much we have yet to discover about our evolving urban environments. As new technologies are developed, deployed, and appropriated, it is critical to ask how they can help us see both the city and our discipline differently. Can architecture and urban design become a multi-species, collaborative practice? The first step is opening our eyes to all of our fellow city dwellers.
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For all of their history, the machines around us have stood silent, but when the city acquires the ability to see, to listen and to talk back to us, what might constitute a meaningful reciprocal interaction? Is it possible to have a productive dialogue with an autonomous shipping crane loading containers into the hull of a ship at a Chinese mega port; or, how do we ask a question of a warehouse filled with a million objects or talk to a city managing itself based on aggregated data sets from an infinite network of media feeds? Consumer-facing AIs like Amazons Alexa, Microsofts Cortana, Google Assistant or Apples Siri repeat biases and forms of interactions which are a legacy of human to human relationships. If you ask Microsofts personal digital assistant Cortana if she is a woman she replies Well, technically I'm a cloud of infinitesimal data computation. It is unclear if Cortana is a she or an it or a they. Deborah Harrison, the lead writer for Cortana, uses the pronoun she when referring to Cortana but is also explicit in stating that this does not mean she is female, or that she is human or that a gender construct could even apply in this context. We are very clear that Cortana is not only not a person, but there is no overlay of personhood that we ascribe, with the exception of the gender pronoun, Harrison explains. We felt that it was going to convey something impersonal and while we didnt want Cortana to be thought of as human, we dont want her to be impersonal or feel unfamiliar either.
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AI (artificial intelligence) can transform the environment we live in. Cities are facing the rise of UI (urban intelligence). Micro sensors and smart handheld electronics can gather large amounts of information. Mobile sensors, referred to as urban tech, allow cars, buses, bicycles, and even citizens to collect information about air quality, noise pollution, and the urban infrastructure at large. For example, noise data can be captured, archived, and made accessible. In an effort to contribute toward urban noise mitigation, citizens will be able to measure urban soundscapes, and urban planners and city councils can react to the data. How will our lives change intellectually, physically, and emotionally as the Internet of Things migrates into urban environments? How does technology intersect with society?
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Thanks to the development of the digital world, cities can be part of natural history. This is our great challenge for the next few decades, The digital revolution should allow us to promote an advanced, ecological and human world. Being digital was never the goalit was a means to reinvent the world. But what kind of world?
In many cases, digital allows us to continue doing everything we invented with the industrial revolution in a more efficient way. Thats why many of the problems that arose with industrial life have been exacerbated with the introduction of new digital technologies. Our cities are still machines that import goods and generate waste. We import hydrocarbons extracted from the subsoil of the earth to make plastics or fuels, which allow us to consume or move effectively while polluting the environment. Cities are also the recipients of the millions of containers filled with products that move around the world, and where we produce waste that creates mountains of garbage.
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We may imagine that one day, when a city was full of sensors to give it the ability of watching and hearing, data could be collected and analyzed as much as possible to make the city run more efficiently. Public space would be better managed to avoid any offense and crime, traffic flows be better monitored to avoid any traffic jam or traffic accident, public services be more evenly distributed to achieve social equity in space, land use be more reasonably zoned or rezoned to achieve a land value as high as possible, and so on. The city would function as a giant machine of high efficiency and rationality that would treat everyone and everything in the city as an element on the giant machine, under the supervision and in line with the values of the hidden eyes and ears. But, the city is not a machine, it is an organism composed of first of all numerous men who are often different one from another, and then the physical environment they create and shape in a collective way. Before the appearance of the city full of sensors, man needs to first work out a complete set of regulations on the utilization of sensors and the data they collect to deal with the issues of privacy and diversity.
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In his bookThe Second Digital Turn, Mario Carpo provides an incisive definition of the difference between artificial intelligence and "human" intelligence. Through the slogan "search, don't sort", he well describes how our way of using email has changed after the spread of Gmail:
We used to think that sorting saves time. It did; but it doesnt any more, because Google searches (in this instance, Gmail searches) now work faster and better. So taxonomies, at least in their more practical, utilitarian modeas an information retrieval toolare now useless. And of course computers do not have queries on the meaning of life, so they do not need taxonomies to make sense of the world, eitheras we do, or did.[Mario Carpo,The Second Digital Turn. Design Beyond Intelligence, MIT Press, Cambridge MA, 2017, p. 25.]
Machine-intelligence is an infinite search based on a finite request: Carpo's machine, which announces the second digital turn (or revolution?), is able to find a needle in a haystack - so long as someone asks it to look for a needle, for reasons that are still human. There is no longer any need for shelves, drawers, or taxonomies to narrow down the search-terms into increasingly coherent sets (as was the case with "sorting"). The machine will find the needle wherever it is, in the chaos of the pseudo-infinite space of the World Wide Web or, in a more general sense, of the "Big Data". It will do so in an instant. And herein lies its intelligence: it can look for a needle in a pseudo-infinite haystack (Big Data) at a very high speed (Big Calcula).
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6 Visions of How Artificial Intelligence will Change Architecture - ArchDaily
Futuristic Impacts of AI Over Businesses and Society – Analytics Insight
In the past decade, artificial intelligence (AI) has made it to mainstream society from academic journals. The technology has achieved numerous milestones when it comes to digital transformation across society including businesses, education, and healthcare as well. Today people can do the tasks which were not even possible ten years back.
The proportion of organizations using AI in some form rose from 10 percent in 2016 to 37 percent in 2019 and that figure is extremely likely to rise further in the coming year, according to Gartners 2019 CIO Agenda survey.
While the breakthroughs in surpassing human ability at human pursuits, such as chess, make headlines, AI has been a standard part of the industrial repertoire since at least the 1980s. Then production-rule or expert systems became a standard technology for checking circuit boards and detecting credit card fraud. Similarly, machine-learning (ML) strategies like genetic algorithms have long been used for intractable computational problems, such as scheduling, and neural networks not only to model and understand human learning but also for basic industrial control and monitoring.
Moreover, AI is also the core of some of the most successful companies in history in terms of market capitalizationApple, Alphabet, Microsoft, and Amazon. Along with information and communication technology (ICT) more generally, the technology has revolutionized the ease with which people from all over the world can access knowledge, credit, and other benefits of a contemporary global society. Such access has helped lead to a massive reduction of global inequality and extreme poverty, for example by allowing farmers to know fair prices, the best crops, and giving them access to accurate weather predictions.
Following the trends, we can say that there will be big winners and losers as collaborative technologies, robots and artificial intelligence transform the nature of work. Moreover, data expertise will become exponentially more important. Across various organizations, the role of a senior manager in a deeply data-driven world is about to shift, thanks to the AI revolution. It is estimated that information hoarders will slow the pace of their organizations and forsake the power of artificial intelligence while competitors exploit it.
In the future, judgments about consumers and potential consumers will be made instantaneously and many organizations will put cybersecurity on par with other intelligence and defense priorities. Besides, open-source information and artificial intelligence collection will provide opportunities for global technological parity and soon predictive analytics and artificial intelligence could play an even more fundamental role in content creation.
With the growth of AI-enabled technologies in the future, societies will face challenges in realizing technologies that benefit humanity instead of destroying and intruding on the human rights of privacy and freedom of access to information. Also, the surging capabilities of robots and artificial intelligence will see a range of current jobs supplanted, where professional roles such as doctors, lawyers, and accountants could be replaced by artificial intelligence by the year 2025.
Moreover, low-skill workers will reallocate to tasks that are non-susceptible to computerization. All the risks will arise out of human activity from certain technological development in this technology, synthetic biology, nano techno, and artificial intelligence.
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Futuristic Impacts of AI Over Businesses and Society - Analytics Insight
As adoption of artificial intelligence accelerates, can the technology be trusted? – SiliconANGLE News
The list of concerns around the use of artificial intelligence seems to grow with every passing week.
Issues around bias, the use of AI for deepfakevideos and audio,misinformation, governmental surveillance, securityand failure of the technology to properly identify the simplest of objects have created a cacophony of concern about the technologys long-term future.
One software company recently released a study which showed only 25% of consumers would trust a decision made by systems using AI, and another report commissioned by KPMG International found that a mere 35% of information technology leaders had a high level of trust in their own organizations analytics.
Its a bumpy journey for AI as the technology world embarks on a new decade and key practitioners in the space are well aware that trust will ultimately determine how widely and quickly the technology becomes adopted throughout the world.
We want to build an ecosystem of trust,Francesca Rossi, AI ethics global leader at IBM Corp., said at the digitalEmTech Digital conference on Monday. We want to augment human intelligence, not replace it.
The EmTech Digital event, restructured into a three-day digital conference by MIT Technology Review after plans to hold it this month in San Francisco were canceled, was largely focused on trust in AI and how the tech industry was seeking to manage a variety of issues around it.
One of those issues is the use of deepfake AI tools to create genuine appearing videos or audio to deceive users. The use of deepfake videos has been rising rapidly, according to recent statistics provided by Deeptrace, which found an 84% rise in false video content versus a year ago.
Today more than ever we cannot believe what we see, and we also cannot believe what we hear,Delip Rao, vice president of research at AI Foundation, said during an EmTech presentation on Tuesday. This is creating a credibility crisis.
To help stem the flow of deepfakes into the content pool, the AI Foundation has launched a platform,Reality Defender, thatuses deepfake detection methods provided by various partners, including Google LLC and Facebook Inc. The nonprofit group recently extended its detection technology to include 2020 election campaigns in the U.S. as well.
As a generation, we have consumed more media than any generation before us and were hardly educated about how we consume it, Rao said. We cannot afford to be complacent. The technology behind deepfakes is here to stay.
AI has also come under fire for its use in facial recognition systems powered by a significant rise in the installation of surveillance cameras globally. A recent report by IHS Markit showed that China leads the world with 349 million surveillance cameras. The U.S. has 70 million cameras, yet it is close to China on a per capita basis with 4.6 people per camera installed.
The rise of AI-equipped cameras and facial recognition software has led to the development of a cottage industry on both sides of the equation. One Chinese AI company SenseTime has claimed the development of an algorithm which can identify a person whose facial features are obscured by a surgical mask and use thermal imaging to determine body temperature.
Meanwhile, a University of Maryland professor has developed a line of clothing, including hoodies and t-shirts, emblazoned with patterns specially designed to defeat surveillance camera recognition systems. All of that underscores the growing societal challenges faced by practitioners in the AI field.
The other complex problem affecting the AI industry involves cybersecurity. As adoption grows and the tools improve, the use of AI is not limited to white hat users. Black hat hackers have access to AI as well and they have the capability to use it.
Cybersecurity vendor McAfee Inc. has seen evidence that hackers may be employing AI to identify victims likely to be vulnerable to attack, according to Steve Grobman, senior vice president and chief technology officer at McAfee. Malicious actors can also use the technology to generate customized content as a way to sharpen spear phishing lures.
AI is a powerful tool for both the defenders and the attackers, Grobman said. AI creates a new efficiency frontier for the attacker. Were seeing a constant evolution of attack techniques.
The trust issues surrounding AI represent an important focus right now because the AI train has left the station and a lot of passengers are on board for the ride. AI has become a key element in improving operational efficiency for many businesses and a number of speakers at the event outlined how enterprises are employing the technology.
Frito Lay Inc. uses AI to analyze weather patterns and school schedules to determine when its corn chip inventory should be increased on store shelves. Global healthcare provider Novartis AG is using AI to support clinical trials and determine injection schedules for people with macular degeneration.
And when engineers at shipping giant DHL International saw how AI could be used to detect cats in YouTube videos, they wondered if the same approach could be taken to inspect shipping pallets for stackability in cargo planes.
These are small decisions were doing for load efficiency on over 500 flights per night, said Ben Gesing, DHLs director and head of trend research. At DHL, no new technology has been as pervasive or as fast-growing as AI.
Perhaps even more intriguing was the recent news that Salesforce Inc. has employed AI to undertake major research on protein generation. Earlier this month, Salesforce published a study which detailed a new AI system called ProGen that can generate proteins in a controllable fashion.
In a presentation Tuesday, Salesforce Chief ScientistRichard Socherdescribed how the company viewed AI as a double-edged strategy. One is the science fiction state, in which dreams of self-driving cars and big medical breakthroughs reside. The other is the electricity state, which uses technology such as natural language understanding to power chatbots.
AI is in this dual state right now, Socher said. At Salesforce, were trying to tackle both of those states. I truly believe that AI will impact every single industry out there.
If Socher is right, then every industry is going to have to deal with a way to engender trust in how it uses the technology. One EmTech speaker presented results from a recent Deloitte study which found that only one in five CEOs and executives polled had an ethical AI framework in place.
There are challenges ahead of us, said Xiaomeng Lu, senior policy manager at Access Partnership. We cant run away. We have to tackle them head on.
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As adoption of artificial intelligence accelerates, can the technology be trusted? - SiliconANGLE News
Role of AI soars in tackling Covid-19 pandemic – BusinessLine
For the first time in a pandemic, Artificial Intelligence (AI) is playing a role like never before in areas ranging from diagnosing risk to doubt-clearing, from delivery of services to drug discovery in tackling the Covid-19 outbreak.
While BlueDoT, a Canadian health monitoring firm that crunches flight data and news reports using AI, is being credited by international reports to be the first to warn its clients of an impending outbreak on December 31, beating countries and international developmental agencies, the Indian tech space too is buzzing with coronavirus cracking activities.
CoRover, a start-up in the AI space that has earlier developed chatbots for railways ticketing platform, has now created a video-bot by collaborating with a doctor from Fortis Healthcare. In this platform, a real doctor from Fortis Healthcare not a cartoon or an invisible knowledge bank will take questions from people about Covid-19.
Apollo Hospitals has come up with a risk assessment scanner for Covid-19, which is available in six languages and guides people about the potential risk of having the virus. The Jaipur-based Sawai Man Singh Hospital is trying out a robot, made by robot maker Club First, to serve food and medicines to patients to lower the exposure of health workers to coronavirus patients.
This is the first time in healthcare that Artificial Intelligence, Machine Learning, and Natural Language Processing are being used to create a Virtual Conversational AI platform, which assists anyone to be able to interact with doctors and have their queries answered unlike other search engines, which do not guarantee the authenticity of information, CoRovers Ankush Sabharwal claimed, while talking of its video-bot, which is likely to be launched soon.
Sabharwal told BusinessLine that answers to numerous questions have been recorded by Pratik Yashavant Patil, a doctor from Fortis Healthcare. In his AI avatar, Doctor Patil will bust myths, chat with you and will probably have answers to a lot of your questions.
Another start-up, Innoplexus AG, headquartered in Germany but founded by Indians, is claiming that its AI-enabled drug discovery platform is helping to arrive at combinations of existing drugs that may prove more efficacious in treating Covid-19 cases.
Its AI platform, after scanning the entire universe of Covid-related data has thrown up results to show that Hydroxycholoroquine or Chroloquine, an anti-malaria drug that is being prescribed as a prophylactic for coronavirus under many protocols works more effectively with some other existing drugs than when it is used alone, the company claims.
Our analysis shows that Chloroquine works more effectively in combination with Pegasys (a drug used to treat Hepatitis C] or Tocilizumab, (a rheumatoid arthritis drug) or Remdesivir (yet to be approved antiviral drug for Ebola) or Clarithromycin (an antibiotic). We are hoping to work with drug regulators and partners to test these in pre-clinical and clinical trials, said Gunjan Bhardwaj, CEO, Innoplexus.
To be sure, hundreds of clinical trials are currently under way with several cocktails of medicines for Covid-19 across the world, and some of these drugs were part of trials held in China and Taiwan. The World Health Organization (WHO) itself is monitoring a global mega clinical trial for testing drugs for Covid-19 called solidarity, which India decided to join on Friday.
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Role of AI soars in tackling Covid-19 pandemic - BusinessLine
Google and the Oxford Internet Institute explain artificial intelligence basics with the A-Z of AI – VentureBeat
Artificial intelligence (AI) is informing just about every facet of society, from detecting fraud and surveillanceto helping countries battle the current COVID-19 pandemic. But AI is a thorny subject, fraught with complex terminology, contradictory information, and general confusion about what it is at its most fundamental level. This is why the Oxford Internet Institute (OII), the University of Oxfords research and teaching department specializing in the social science of the internet, has partnered with Google to launch a portal with a series of explainers outlining what AI actually is including the fundamentals, ethics, its impact on society, and how its created.
The Oxford Internet Institute is a multidisciplinary research and teaching department of the University of Oxford, dedicated to the social science of the Internet.
At launch, the A-Z of AI covers 26 topics, including bias and how AI is used in climate science, ethics, machine learning, human-in-the-loop, and Generative adversarial networks (GANs).
Googles People and AI Research team (PAIR) worked with Gina Neff, a senior research fellow and associate professor at OII, and her team to select the subjects they felt were pivotal to understanding AI and its role today.
The 26 topics chosen are by no means an exhaustive list, but they are a great place for first-timers to start, the guides FAQ section explains. The team carefully balanced their selections across a spectrum of technical understanding, production techniques, use cases, societal implications, and ethical considerations.
For example, bias in data sets is a well-documented issue in the development of AI algorithms, and the guide briefly explains how the problem is created and how it can be addressed.
Typically, AI forms a bias when the data its given to learn from isnt fully comprehensive and, therefore, starts leading it toward certain outcomes, the guide reads. Because data is an AI systems only means of learning, it could end up reproducing any imbalances or biases found within the original information. For example, if you were teaching AI to recognize shoes and only showed it imagery of sneakers, it wouldnt learn to recognize high heels, sandals, or boots as shoes.
You can peruse the guide in its full A-Z form or filter content by one of four categories: AI fundamentals, Making AI, Society and AI, and Using AI.
Those with a decent background in AI will find this guide simplistic, but its a good starting point for anyone looking to grasp the key points they will be hearing about as AI continues to shape society in the years to come.
Its also worth noting that this isnt a static resource the plan is to update it as AI evolves.
The A-Z will be refreshed periodically as new technologies come into play and existing technologies evolve, the guide explains.
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Google and the Oxford Internet Institute explain artificial intelligence basics with the A-Z of AI - VentureBeat