Page 3,133«..1020..3,1323,1333,1343,135..3,1403,150..»

Singapore tops cloud adoption in ASEAN – ComputerWeekly.com

Singapore is the most prolific adopter of cloud computing in Southeast Asia, with nearly nine in 10 IT decision-makers saying their companies are already using cloud-based services, a new survey has found.

According to Alibaba Clouds Cloud in Asia survey conducted in November 2020, seven in 10 respondents in Singapore also said the hybrid cloud approach was key to their companys disaster recovery and business continuity efforts amid the Covid-19 pandemic.

When it comes to top concerns about cloud adoption, Singapore respondents were more concerned with cost and security (57%) and availability (49%), while their regional counterparts said integrating cloud services into existing IT infrastructure was one of the key issues.

The acceleration of digital transformation across the region has increased the rate of cloud adoption. In Singapore, over 70% of respondents said their company was using more cloud services than before, with 72% noting that the cloud could help to tackle a sudden and devastating event such as Covid-19.

Derek Wang, Singapore general manager at Alibaba Cloud Intelligence, said the maturity of IT and cloud infrastructure and attitudes in Singapore meant it was no longer a question of creating more clouds, but how to use existing ones more effectively.

Having realised much of the immediate benefits of moving to the cloud, the challenge for businesses here is to create new cloud-based solutions to avoid the diminishing returns trap, he said.

In Singapore, 15% of respondents said cloud and other digitisation efforts did not help their company cope with the pandemic the highest figure in all markets surveyed. About one-third also said their companies worked independently to develop new solutions to mitigate pandemic-driven needs, such as remote working.

Technology implementation in Singapore can be costly and time-consuming, especially for SMEs, said Wang. Many of them are lacking the right talent and resources to take full advantage of the cloud. This is one of the major sources of dissatisfaction based on our observation.

As a global trusted cloud service provider, we are here to help businesses address their pain-points and reduce their learning curve effectively.

During Singapores lockdown period last year, Alibaba Cloud reported an increase of more than 50% in demand for its cloud computing technologies from companies in industries such as retail, education and logistics.

These included local technology firm SCash, which has been working with Alibaba since the Covid-19 outbreak to help local enterprises embark on digital transformation.

SCash technical director Edric Khoo said: Working with a hyperscale cloud service provider enables us to pass on the benefits from economies of scale to our customers, and with our local expertise, we can customise the best-fit solutions for our customers in order to help them achieve the most from cloud computing technologies.

During the third quarter of its 2021 fiscal year, Alibabas cloud computing revenue grew by 50% year on year to RMB16.115bn ($2.47bn), primarily driven by demand from internet, retail and public sector customers.

For the first time, Alibaba Cloud also achieved positive adjusted Ebita (earnings before interest, taxes, and amortisation) during the quarter, thanks to economies of scale.

Alibaba migrated its e-commerce businesses to its public cloud a year ago, and the move has enabled it to cope with peak periods such as Singles Day, when peak orders hit 583,000 per second last year.

More:
Singapore tops cloud adoption in ASEAN - ComputerWeekly.com

Read More..

Rapid Insight to Present at Data Science Salon’s Healthcare, Finance, and Technology Virtual Event – PR Web

Rapid Insight to Present at Data Science Salons Healthcare, Finance, and Technology Virtual Event

CONWAY, N.H. (PRWEB) February 12, 2021

On February 17th, Rapid Insight, a leading data analytics software provider, will co-present a Data Science Salon session with Dr. Michael Johnson, Senior Data Scientist at St. Charles Health Center. The session, titled Keeping Humans in the Loop: 4 Strategies for Optimizing the Impact of Data Science in your Organization, will cover techniques to amplify data science and cultivate a data-informed mentality across an organization. The presentation will feature practical advice and real-world examples from Dr. Johnsons work modeling the pandemic and coordinating vaccination logistics. James Cousins, Rapid Insights Analyst Manager, will co-host the presentation.

Rapid Insight is an intermediate sponsor for Data Science Salons upcoming February virtual event. The event, titled Applying AI and Machine Learning to Healthcare, Finance, and Technology, will connect analysts and data scientists from three major industries for illuminating conversations in a casual environment. As a first-time sponsor of the event, Rapid Insight will offer a unique perspective on improving data efficiency and operationalizing business intelligence. Rapid Insight will host a virtual booth where visitors can speak with Rapid Insight analysts, schedule a software demo, and access resources to learn more about the products.

Data Science Salon events are opportunities for professionals to discuss the real issues and questions they confront in their day-to-day work, said Mike Laracy, Rapid Insights Founder and President. Our tools and support are specifically designed to make the work of data scientists and analysts more efficient, so were thrilled to join the conversation and contribute to the body of knowledge with our presentation. Dr. Michael Johnson is one of our most impressive and knowledgeable users, and were excited for him to share his wisdom with Salon attendees.

Amidst the tumult of the past 12 months, data science has been essential. Organizations rapidly adapted procedures due to COVID-19 and looked to data scientists for guidance. Data Science Salon events present a unique opportunity to share insight and wisdom within the data community. As a solutions provider designed to enable teams of all sizes to succeed, Rapid Insight is excited to engage with Data Science Salon attendees to learn and share along with them.

About Rapid Insight:Rapid Insight is a leading provider of business intelligence and automated predictive analytics software. With a specialty in higher education and a focus on ease of use and efficiency, Rapid Insight products enable users to turn their raw data into actionable information. The companys analytic software simplifies the extraction and analysis of data, enabling institutions with student populations of all sizes to fully utilize their information for data-informed decision making. For more information, visit http://www.rapidinsight.com.

###

Share article on social media or email:

Go here to read the rest:

Rapid Insight to Present at Data Science Salon's Healthcare, Finance, and Technology Virtual Event - PR Web

Read More..

9 investors discuss hurdles, opportunities and the impact of cloud vendors in enterprise data lakes – TechCrunch

About a decade ago, I remember having a conversation with a friend about big data. At the time, we both agreed that it was the purview of large companies like Facebook, Yahoo and Google, and not something most companies would have to worry about.

As it turned out, we were both wrong. Within a short time, everyone would be dealing with big data. In fact, it turns out that huge amounts of data are the fuel of machine learning applications, something my friend and I didnt foresee.

Frameworks were already emerging like Hadoop and Spark and concepts like the data warehouses were evolving. This was fine when it involved structured data like credit card info, but data warehouses werent designed for unstructured data you needed to build machine learning algorithms, and the concept of the data lake developed as a way to take unprocessed data and store until needed. It wasnt sitting neatly in shelves in warehouses all labeled and organized, it was more amorphous and raw.

Over time, this idea caught the attention of the cloud vendors like Amazon, Microsoft and Google. Whats more, it caught the attention of investors as companies like Snowflake and Databricks built substantial companies on the data lake concept.

Even as that was happening startup founders began to identify other adjacent problems to attack like moving data into the data lake, cleaning it, processing it and funneling to applications and algorithms that could actually make use of that data. As this was happening, data science advanced outside of academia and became more mainstream inside businesses.

At that point there was a whole new modern ecosystem and when something like that happens, ideas develop, companies are built and investors come. We spoke to nine investors about the data lake idea and why they are so intrigued by it, the role of the cloud companies in this space, how an investor finds new companies in a maturing market and where the opportunities and challenges are in this lucrative area.

To learn about all of this, we queried the following investors:

Caryn Marooney: The data market is very large, driven by the opportunity to unlock value through digital transformation. Both the data lake and data warehouse architectures will be important over the long term because they solve different needs.

For established companies (think big banks, large brands) with significant existing data infrastructure, moving all their data to a data warehouse can be expensive and time consuming. For these companies, the data lake can be a good solution because it enables optionality and federated queries across data sources.

Dharmesh Thakker: Databricks (which Battery has invested in) and Snowflake have certainly become household names in the data lake and warehouse markets, respectively. But technical requirements and business needs are constantly shifting in these markets and its important for both companies to continue to invest aggressively to maintain a competitive edge. They will have to keep innovating to continue to succeed.

Regardless of how this plays out, we feel excited about the ecosystem thats emerging around these players (and others) given the massive data sprawl thats occurring across cloud and on-premise workloads, and around a variety of data-storage vendors. We think there is a significant opportunity for vendors to continue to emerge as unification layers between data sources and different types of end users (including data scientists, data engineers, business analysts and others) in the form of integration middleware (cloud ELT vendors); real-time streaming and analytics; data governance and management; data security; and data monitoring. These markets shouldnt be underestimated.

Casey Aylward: There are a handful of big opportunities in the data lake space even with many established cloud infrastructure players in the space:

See the article here:

9 investors discuss hurdles, opportunities and the impact of cloud vendors in enterprise data lakes - TechCrunch

Read More..

Following the COVID science: what the data say about the vaccine, social gatherings and travel – Chicago Sun-Times

The U.S. is inching closer to herd immunity almost two months into the COVID-19 vaccine rollout, with more than one million Americans getting vaccinated per day, according to the Centers for Disease Control and Prevention.

But with a large portion of the population still waiting to get vaccinated and questions around asymptomatic spread, immunized Americans wonder: Is it safe to leave the house and live a pre-pandemic lifestyle?

Not just yet, experts say.

Getting the vaccine is not a free pass to put aside all the public health measures officials have been reiterating since the beginning of the pandemic, Dr. Anthony Fauci said in a CNN town hall in January.

We dont want people to think that just because theyre vaccinated that other public health recommendations just dont apply, said the nations leading infectious disease expert.

But, there is light at the end of the tunnel. Each vaccination gets the U.S. closer to herd immunity and closer to easing restrictions and returning to normal, health experts say. Until then, social gatherings and travel without protective measures could jeopardize how quickly that may happen.

Data shows small gatherings drive transmission as people tend to relax safety precautions such as masking and social distancing around close friends and family, said Dr. Wafaa El-Sadr, professor of epidemiology and medicine at Columbia University Mailman School of Public Health.

Even when a person is vaccinated, it takes up to two weeks to reach maximum immunity and no shot offers total protection.

Recent data also shows the COVID-19 vaccines may be less effective against new coronavirus variants, specifically one that originated in South Africa. As of Wednesday, the U.S. reported 932 cases of the U.K. variant and nine cases of the South African variant, according to CDC data. The agency said the U.K. variant, called B.1.1.7, could become the dominant strain by March.

Colleges around the U.S. have canceled spring break to discourage students from traveling after celebrations around the same time last year led to a summer surge of coronavirus infections.

Traveling is one of the fastest ways to spread the coronavirus, experts say, and unfortunately, we still dont know if the COVID-19 vaccine protects against transmission.

While studies show the vaccines are effective against symptomatic disease, researchers are still learning its impact on asymptomatic infection. For this reason, health officials warn against non-essential travel even after getting vaccinated.

You can conceivably get infected, get no symptoms and still have virus in your nasal pharynx, Fauci said during the town hall. Its possible that while carrying that virus, someone can transmit it to other travelers, family or friends.

Were in a race between the vaccines and a race with the virus, and its a moment in time where theres a lot of unknowns, El-Sadr said.

While some states have already begun to lift COVID-19 restrictions on restaurants, weddings and even indoor entertainment, health experts say its too early to attend social gatherings without protection.

After a year of pandemic restrictions, Americans are eager to get out of the house, El-Sadr said. But she urges Americans to continue masking and social distancing.

Whatever you were doing the day before you got vaccinated, you continue to do the day after you get vaccinated, El-Sadr said.

If people have to get together, they should minimize risk by being outside, wearing a mask and social distancing, said Dr. Sarita Shah, associate professor of at the department of global health, epidemiology and infectious diseases at Emory University.

We can get together in these small groups using these safety steps that we all know work, she said.

While getting the COVID-19 vaccine doesnt mean a sudden return to pre-pandemic ways, it could mean less anxiety and more individual freedoms.

Experts disagree on exactly how much freedom, but Dr. Vinay Prasad, an associate professor of epidemiology and biostatistics at the University of California San Francisco, argues theres little risk in dining with a fellow vaccinated friend indoors or hugging fully immunized grandparents.

Nothing in this world comes with 0% risk, he adds, but one can drastically diminish risk by getting vaccinated. After that, its up to the individual to assess their own risk comfortability.

No one is chasing a zero-risk life. In fact, that is a mirage, Prasad said said in an op-ed on Medpage Today. Instead, we all want reasonable safety.

Spring travel may be possible if its done safely and travelers are mindful of where theyre going and who theyre seeing. People should avoid traveling to an area where infections are on the rise and visiting loved ones who are vulnerable to severe disease and not vaccinated.

President Joe Biden signed an executive order in his first days in office mandating masks in flights, trains and buses. The Transportation Security Administration announced last week that it will recommend fines ranging from $250 to $1,500 for people who do not abide by the new transportation mask order.

The CDC issued guidelines Wednesday recommending wearing a surgical mask underneath a cloth mask or knotting the surgical masks to prevent air seeping through the sides.

Shah doesnt expect coronavirus cases to increase dramatically like it did after the holidays as more Americans will be vaccinated and warmer weather will hopefully push people to host gatherings outside.

On Memorial Day, were going to have a different scenario, she said. The first and best thing is that its warmer and people will be outside. That reduces the risk a lot.

The Biden administration is on track to administer 100 million vaccine doses in 100 days. But even after accomplishing this goal, the U.S. will still be far from achieving herd immunity, said CDC director Rochelle Walensky.

Its going to take a while for us to feel like were back to a sense of normalcy, she said during the CNN town hall. After we vaccinate 100 million Americans, were going to have 200 million more to vaccinate.

Read more at usatoday.com

Continue reading here:

Following the COVID science: what the data say about the vaccine, social gatherings and travel - Chicago Sun-Times

Read More..

Automated Data Science and Machine Learning Platforms Market Technological Growth and Precise Outlook 2021- Microsoft, MathWorks, SAS, Databricks,…

Global Automated Data Science and Machine Learning Platforms Market Size, Status and Forecast 2021

The Global Automated Data Science and Machine Learning Platforms Market Research Report 2021-2026 is a valuable source of insightful data for business strategists. It provides the industry overview with growth analysis and historical & futuristic cost, revenue, demand, and supply data (as applicable). The research analysts provide an elaborate description of the value chain and its distributor analysis. This Market study provides comprehensive data that enhances the understanding, scope, and application of this report.

Click the link to get a Sample Copy of the Report:

https://www.marketinsightsreports.com/reports/01122519203/global-automated-data-science-and-machine-learning-platforms-market-growth-status-and-outlook-2020-2025/inquiry?Mode=P68

Market Segmentation:

Key Players:Palantier, Microsoft, MathWorks, SAS, Databricks, Alteryx, H2O.ai, TIBCO Software, IBM, Dataiku, Domino, Altair, Google, RapidMiner, DataRobot, Anaconda, KNIME and others.

Segment by Types:Cloud-based

On-premises

Segment by Applications:Small and Medium Enterprises (SMEs)

Large Enterprises

Regions Are covered By Automated Data Science and Machine Learning Platforms Market Report 2021 To 2026

For comprehensive understanding of market dynamics, the global Automated Data Science and Machine Learning Platforms market is analyzed across key geographies namely: North America (United States, Canada, and Mexico), Europe (Germany, France, UK, Russia, and Italy), Asia-Pacific (China, Japan, Korea, India, and Southeast Asia), South America (Brazil, Argentina, and Colombia), Middle East and Africa (Saudi Arabia, UAE, Egypt, Nigeria, and South Africa). Each of these regions is analyzed on the basis of market findings across major countries in these regions for a macro-level understanding of the market.

Key Highlights of the Report

Quantitative market information and forecasts for the global Automated Data Science and Machine Learning Platforms industry, segmented by type, end-use, and geographic region.

Expert analysis of the key technological, demographic, economic, and regulatory factors driving growth in the Automated Data Science and Machine Learning Platforms to 2026.

Market opportunities and recommendations for new investments.

Growth prospects among the emerging nations through 2026.

Browse Full Report at:

https://www.marketinsightsreports.com/reports/01122519203/global-automated-data-science-and-machine-learning-platforms-market-growth-status-and-outlook-2020-2025?Mode=P68

There are 13 Sections to show the global Automated Data Science and Machine Learning Platforms market:

Chapter 1:Market Overview, Drivers, Restraints and Opportunities, Segmentation overviewChapter 2:Market competition by ManufacturersChapter 3:Production by RegionsChapter 4:Consumption by RegionsChapter 5:Production, By Types, Revenue and Market share by TypesChapter 6:Consumption, By Applications, Market share (%) and Growth Rate by ApplicationsChapter 7:Complete profiling and analysis of ManufacturersChapter 8:Manufacturing cost analysis, Raw materials analysis, Region-wise manufacturing expensesChapter 9:Industrial Chain, Sourcing Strategy and Downstream BuyersChapter 10:Marketing Strategy Analysis, Distributors/TradersChapter 11:Market Effect Factors AnalysisChapter 12:Market ForecastChapter 13:Automated Data Science and Machine Learning Platforms Market Research Findings and Conclusion, Appendix, methodology and data source

Finally, researchers throw light on the pinpoint analysis of Global Automated Data Science and Machine Learning Platforms Market dynamics. It also measures the sustainable trends and platforms which are the basic roots behind the market growth. The degree of competition is also measured in the research report. With the help of SWOT and Porters five analysis, the market has been deeply analyzed. It also helps to address the risk and challenges in front of the businesses. Furthermore, it offers extensive research on sales approaches.

Note: All the reports that we list have been tracking the impact of COVID-19. Both upstream and downstream of the entire supply chain has been accounted for while doing this. Also, where possible, we will provide an additional COVID-19 update supplement/report to the report in Q3, please check for with the sales team.

ABOUT US:

MarketInsightsReports provides syndicated market research on industry verticals including Healthcare, Information, and Communication Technology (ICT), Technology and Media, Chemicals, Materials, Energy, Heavy Industry, etc.MarketInsightsReportsprovides global and regional market intelligence coverage, a 360-degree market view which includes statistical forecasts, competitive landscape, detailed segmentation, key trends, and strategic recommendations.

CONTACT US:

Irfan Tamboli (Head of Sales) Market Insights Reports

Phone: + 1704 266 3234 | +91-750-707-8687

sales@marketinsightsreports.com |irfan@marketinsightsreports.com

Originally posted here:

Automated Data Science and Machine Learning Platforms Market Technological Growth and Precise Outlook 2021- Microsoft, MathWorks, SAS, Databricks,...

Read More..

Opinion: How to secure the best tech talent | Human Capital – Business Chief

The pandemic has created a frenzied expansion of DevOps roles fuelled by cloud-based activity, and the automation of different tech stacks to deploy and write code. Faced with managing the advent of so much more cloud-based development, companies are frantically looking for DevOps professionals with the experience required to maintain continuous and even accelerated development.

There are other tech roles also in high demand. Its no secret that a great data scientist is hard to come by, but an all-in-one data wizard who possesses every skill required to conceptualize, create, maintain and productionalize successful data models that can drive business decisions is a rare breed indeed.

Those worth their salt are frequently snapped up by multinationals, leaving a small subsection who may prefer to work for a tech start up or scale up however, they may need convincing.

What can tech companies actually do to secure the best talent in an age where hiring just got harder?

Heres a trade secret: when it comes to complex roles that are in high demand, you would think it would be very difficult to attract the right candidates. Yet in fact, the opposite is actually true. Its actually remarkably easy to target these people because they are digital natives, and likely to be present on digital marketing channels.

Because tech people have specific skills that are related to specific technologies, it makes sense to use these as keywords in digital marketing campaigns. For instance, if youre recruiting for DevOps roles and you throw out buzzwords Amazon AWS, Azure, RedShift, and digital products like Jenkins if you target recruitment activities against these words, youre going to hit DevOps people. Similarly, if you target keywords like Hadoop and Python, youre going to hit data scientists.

If firms can embrace digital attraction, and use it as an element in a dedicated recruitment marketing strategy, it may go a long way to ensuring that the correct people see the job adverts and campaigns - and to make sure recruitment budget is not wasted on unqualified candidates.

Social media can also be a big help in targeting people who meet specific criteria. While traditional job board adverts can be seen by anyone who uses appropriate search terms, targeted social media ads can only be seen by people who meet specific criteria. This could be qualifications, experience, specific skill sets or even location; with Facebook, LinkedIn and Google targeting capabilities, there are a range of possibilities which can ensure only suitable candidates see your ads.

The data science polymath is about to become as rare as another Newton or Goethe. Instead of trying to hire multiple unicorns, organizations may be better off following a more flexible approach to getting the data science talent they need.

Its better to use your own best people to define the data sub-specialisms and hire against that. The data scientist is a new and evolving position, and the same criteria will not likely fit for everyone. With data science roles, you have to have an evolving view of what a senior, intermediate or junior position looks like and requires.

Someone may have done a masters degree in data science, but the fact is, these courses for the most part have only been around for a few years. By the checkbox criteria, that person may not yet have the skills to be a data scientist in a lot of organizations. Its a new discipline which will fragment over time, and specialty qualifications will emerge within it. Organizations must understand the complexities of the roles and be more flexible in their hiring practices in order to secure good data science talent.

Recruiting for highly specialized roles is a bit of a new discipline, and companies need to begin to look at non-standard routes to hiring. Limiting roles so severely and only considering, for example, Ivy League or Oxbridge maths graduates with several years worth of experience is unlikely to end up in success. There may be candidates with 10 years of experience or industry veterans who are perfect, but who did not attend the very top elite schools.

Beyond this, online education is improving at a Moores Law type of pace. Components of data science can be learned to a practical level without the need for a STEM PhD. Hiring for raw aptitude or transferable skills can pipeline talent for scale.

LinkedIn is a useful recruitment platform, and their paid job ads are pretty good, but there's a key flaw. The key flaw with LinkedIn is that it only addresses the active candidate market. If there are five million software developers in the US, only 15 to 20% of them may be actively looking for positions.

Relying wholly on platforms like LinkedIn means you've ignored 80% of potential candidates. With the right message and the right communication, tech companies may be able to attract someone who wasnt even looking for a new role in the first place. Its a bit like advertising: the same way companies win new customers for products people didnt even know they needed or wanted, companies may be able to attract new candidates.

When it comes to hiring and scaling, tech companies certainly have their work cut out for them these days, but with a little innovation, and an open minded and flexible approach, firms can be well positioned to attract the very best tech talent, and to continue scaling products competitively.

Follow this link:

Opinion: How to secure the best tech talent | Human Capital - Business Chief

Read More..

How Can We Fix the Data Science Talent Shortage? Machine Learning Times – The Predictive Analytics Times

Originally published in Springboard Blog, Jan 22, 2021.

Data science might just be themost buzzed-about job in tech right now, but its pop culture sheen conceals some of the harsh realities of being a fresh graduate in the industry.

The jobtopped LinkedIns yearly Emerging Jobs Report from 2016 to 2019 consecutively (it isnow at #3). But when Springboard data science alum Kristen Colley started hunting for her first data science job in 2019, most companies were not interested in her data science credentials.When I started rebranding myself as a data analyst with the ability to handle machine learning problems, thats when the opportunities started coming in, she said.

Colleys experience is part of an emerging trend in the way companies hire data scientists. With the mainstreaming of automated machine learning (autoML) andDataRobot, an AI platform which can train and tune machine learning models, businesses dont necessarily need full-fledged data scientists who can perform end-to-end data processing, from exploratory data analysis to building ETL pipelinesat least not for junior roles.

If you want that high-paying data science job you signed up for, youre going to have to wait a few years, said Hobson Lane, aSpringboard data science mentorand co-founder ofTangible AI. Theyre moving up the skill level chain because they can now get much of what they need for data science from DataRobot and autoML.

To continue reading this article, click here.

Read the original here:

How Can We Fix the Data Science Talent Shortage? Machine Learning Times - The Predictive Analytics Times

Read More..

Needed: People To Put The Intelligence In Artificial Intelligence – Forbes

People put the intelligence in artificial intelligence

Is the digital workforce ready to take over? Well, not quite. Artificial intelligence may be capable of assuming many tasks, but it will be some time, if ever, that it could replace jobs on a widespread basis. It simply has too many limitations.

Instead, we need to acquaint a generation of workers with technologies to take on the more mundane, repetitive portions of their jobs, and in turn elevate their decision-making roles within enterprises. Thats the word from Steve Shwartz,AI author, researcher and investor, who points out that the notion of AI taking jobs is a myth. However, AI will have a profound impact on employment.

Shwartz, author of the just-published book Evil Robots, Killer Computers, and Other Myths: The Truth About AI and the Future of Humanity, points out that many people are concerned that intelligent robots will be able to read manuals, take courses, and eliminate all our jobs. Fortunately, this is science fiction.

Todays AI systems are only capable of learning functions that relate a set of inputs to a set of outputs, he says. This simple paradigm has enabled fantastic technological accomplishments such as facial recognition, language translation, and cars that can see and avoid pedestrians. However, these learned functions have no more intelligence than a function that translates Fahrenheit temperatures to Celsius temperatures.

It would take a huge breakthrough to create intelligent robots, and todays AI researchers have only vague ideas about how to create such a breakthrough, Shwartz says. Such a breakthrough is about as likely as time travel.

The bottom line is that any job that requires commonsense reasoning is safe; probably for our lifetimes. Maybe forever, he continues. People-oriented skills in finance, marketing, sales, and HR are probably safe. The types of jobs that will be impacted and not necessarily negative impacted are ones that involve repetitive decision-making that can be learned by AI systems.

Rather than replace jobs, AI is replacing tasks especially repetitive, data-oriented analyses are candidates for automation by AI systems. If it is possible to create a large training set of examples in which each example is labeled with the correct answer, that analysis can likely be learned by an AI system, says Shwartz.

Another task category that AI will enhance is repetitive customer service interactions, he continues. AI-based chatbots are assuming more customer-service work, and customer service jobs that involve a human following a script to interact with customers are at the most risk. Human interactions that require real, unscripted conversations are not at risk.

For non-technical careers, the greatest impact is the availability of massive amounts of data, Shwartz says. The field of marketing has already been transformed by data. Marketers analyze data from Google to determine which keywords to buy. They analyze huge amounts of customer data to determine which campaigns should be targeted to which customers. And they analyze massive databases of web traffic to determine what changes to make to their websites. Todays marketers need to be data analysts. Most companies are relying more and more on data to drive the business. Many formerly non-technical jobs now require extensive data analysis. Workers who do not adapt will be left behind.

While AI will be replacing many repetitive tasks and amplifying intelligence through data, the most exciting opportunities will be seen with the creation of new types of businesses. Shwartz was a founder of one of the first AI companies, Cognitive Systems, in 1986. As an angel investor, Shwartz now sees large numbers of startups whose business models are only possible because of AI technology: Computer-vision technology enables computers and robots to identify objects, faces, and activities. Startups are developing in-store products that identify customers and provide highly personalized offers direct to their smartphones. Companies are developing surveillance products for law enforcement and the military. Startups are creating AI-based medical applications to read MRIs and diagnose diseases. Other vendors are using other types of AI technology to detect fraud and stop cyber-attacks, analyze legal documents, predict the weather, improve search results, and even design golf clubs.

Along with achieving greater sophistication and better mimicking human reasoning, AI also brings additional challenges, Shwartz relates. Computer-vision systems have been shown to be biased against minorities. It is not only unethical for companies to roll out biased systems, but also bad for business. In Europe, due to GDPR regulations, it is illegal and similar regulations are almost certain to follow in the US. These biases are often created inadvertently using biased data. Ensuring systems are non-discriminatory can be harder than developing the technology in the first place.

Read more:
Needed: People To Put The Intelligence In Artificial Intelligence - Forbes

Read More..

Elon Musk Talks Auto Safety and Regulation of Artificial Intelligence with Joe Rogan – Corporate Crime Reporter

On the Joe Rogan podcast this week, Tesla CEO Elon Musks inner Ralph Nader was on full display, with Musk promoting federal regulation of artificial intelligence, criticizing the auto industrys campaign against seat belts and safety regulation, and praising modern airbags as crazy good.

In the middle of a three and a half hour conversation, Rogan triggered the discussion on regulation when he said he was worried about artificial intelligence.

We should have oversight of some kind, Musk said. A regulatory agency like the FAA (Federal Aviation Administration) or the FDA (Food and Drug Administration). We need an acronym to oversee this stuff.

Rogan expressed doubts about a government agency getting the job done.

The probability of industry capture is higher if its an industry body than if it is the government, Musk said. Its not zero if it is the government. There are plenty of instances of regulatory capture of a government agency. But the probability is lower than if it is an industry group. At the end of the day somebody has to go and tell Facebook, or Google or Tesla, this is okay or it is not okay. Or at least report back to the public this is what we found. Otherwise the inmates are running the asylum. And these are not necessarily friendly inmates.

Im not a fan of lets have the government do lots of things, Musk said. You want to have the government do the least amount of stuff. The right role of government is for it to be the referee on the field. When the government starts being a player on the field, thats problematic. Or when you start having more referees than players, which is the case in California, then thats not good. You cant have no referees. Everyone agrees that a referee might be annoying at times, but it is better to have a referee than not.

Rogan said Im just worried that its going to be too late, by the time these things become sentient, by the time they develop the ability to analyze what the threat of human beings are and whether or not human beings are essential

Im not saying that having regulatory agencies is some panacea or reduces the risk to zero, Musk said. There is still some significant risk even with a regulatory agency. Nonetheless, the good outweighs the bad and we should have one.

It took a while before there was an FAA, Musk said. There were a lot of plane companies cutting corners. It took a while before there was an FDA. What tends to happen is some company gets desperate, they are on the verge of bankruptcy and they are like we will just cut this corner, it will be fine. And then, somebody dies.

Look at seat belts. Now we take seat belts for granted. But the car companies fought seat belts like there was no tomorrow.

Really, they fought them? Rogan asked.

For decades, Musk said. The data was absolutely clear that you needed seat belts. The difference in fatalities with seat belts versus not seat belts is gigantic and obvious. Its not subtle. But still, the car companies fought seat belts for ten to twenty years. A lot of people died.

Now, these days with advanced airbags, I think we might have come full circle and no longer need seat belts if you have advanced airbags.

What if the car flips? Rogan asked.

You are just covered its airbags everywhere, Musk said. Modern airbags are so good it will blow your mind how good they are. At Tesla, we even update the software to improve how the airbags deploy. We will calculate are you an adult, how much do you weigh, are you sitting in this part of the seat or that part of the seat? You may be a baby. Are you a toddler?

Based on the weight? Rogan asked.

Not just the weight, but the pressure distribution on the seat. Are you sitting on the edge of your seat? Are you a fifth percent female or 95 percent male? The airbag firing will be different depending on where you are sitting on the seat, what size you are, and what your orientation is. And well update it over the years. It gets better over time.

A child could be sitting in the front seat? Rogan asked.

Unbelted child sitting in a bad position probably still fine, Musk said. The seat belt is like if you wear the seat belt thats nice. The airbag is doing the work. Airbag technology is crazy good. You want the airbag to inflate and then deflate, otherwise you are going to be asphyxiated.

We go way beyond the regulatory requirements. We got the lowest probability of injury of any cars they ever tested.

We get five stars in every category and subcategories. And if there was a sixth star, we would get a sixth star.

But then Musk admitted the star safety rating is kind of bullshit.

If a smart car hits a freight train, it doesnt matter how good your safety system is, you are screwed. If you are in a little car and it gets hit by a big car, the big car will win. A low star rating in a big car hitting a high star rating in a small car the small car is screwed. Small cars are not safe.

What about your small car? Rogan asked.

Our Model 3 is not small, Musk said.

What about the Roadster? Rogan asked.

The Roadster is not super safe, Musk said. The original Roadster is not super safe. Its safe for a car like that, but safety maximization is not the goal in a sports car.

See more here:
Elon Musk Talks Auto Safety and Regulation of Artificial Intelligence with Joe Rogan - Corporate Crime Reporter

Read More..

Artificial Intelligence to Improve the Shipping Industry’s Efficiency – The Maritime Executive

MOL's 300,000 dwt Brasil Maru bulk carrier (Mitsui O.S. K. Lines)

By The Maritime Executive 02-10-2021 05:39:48

Efforts are progressing to harness emerging technologies to improve the efficiency of shipping operations. Japans Mitsui O.S.K. Lines announced that it is expanding its efforts with artificial intelligence to achieve greater efficiency with routing which will also contribute to lower emissions from their ships.

Building on a partnership that began in 2019, MOL is working with Bearing, a Silicon Valley-based AI technology startup, to improve efficiencies within the maritime industry. Together the two companies are developing a range of products that combine MOL's maritime expertise and Bearing's AI technology infrastructure.

Bearing, according to MOL, is building technologies using highly-accurate ship performance models built off of a diverse set of real-world data points. These AI-powered models with some historical voyage data for certain vessels such as vessel speed, trim, main engine operation, weather, and sea condition allow Bearing to predict metrics like fuel consumption with state-of-the-art accuracy even without vessels' design parameters.

Through various trials and intensive discussions concerning ship modeling, MOL announced that it has developed an AI-powered Smart Routing Engine. This application automatically analyzes multiple potential routes for a given voyage and recommends prudent, efficient routing through the use of optimal main engine output and propeller RPM profiles.

MOL says that it continuously monitors the condition of its fleet to ensure optimum operational efficiency which is being further aided by combining the technologies of Bearing as well as other existing and new solutions. Through the addition of AI technology to the existing voyage routing systems, MOL expects that it will be able to further enhance the operations of its fleet which currently numbers approximately 800 ships in operation.

MOL says that it understands the transformative potential of AI and looks forward to leveraging Bearings AI expertise and background in building scalable AI technology products to further advance operating efficiencies.

Follow this link:
Artificial Intelligence to Improve the Shipping Industry's Efficiency - The Maritime Executive

Read More..