BETHESDA, Md., Dec. 04, 2018 (GLOBE NEWSWIRE) -- Forward-looking organizations can’t afford to overlook the emerging trends in AI and analytics in 2019. In 8 Most Important AI and Analytics Trends for 2019, DMI unpacks the driving forces in AI for the New Year.

 

“Companies that fail to adopt AI will lose out,” says Dr. Srinivasa Vegi, DMI executive vice president for data analytics and artificial intelligence. “Some industries may even be wiped out.” Artificial intelligence will deliver approximately $2 trillion worth of business value worldwide over the next year through the use of advanced computing algorithms that identify and optimize business insights humans cannot spot.

Be on the look-out for these hot trends:

1) AI and Analytics Merger: Applying AI algorithms to analytics will prove transformative but the complex merger requires a roadmap.

2) Decision Automation: Greater AI and machine learning in enterprise resource planning will empower smarter process changes, minus the human intervention, to drive cost savings.

3) Digital Twins: Digital replicas of physical processes, like production lines, let humans interact with IoT sensors automating asset management.

4) Edge Computing: Data center AI and analytics will deliver more real-time intelligence, anticipating problems and implementing fixes before costly breakdowns.

5) Mixed Reality: The combination of elements of virtual and augmented reality with data analytics will grow in 2019 but will accelerate even faster over the next two or three years.

6) Blockchain Boom: Blockchain uses a shared digital ledger that’s impossible for hackers to breach, making it one of the most-watched technologies of 2019.

7) Cloud Maturity: Thanks to advances in security, more organizations will embrace the cloud generating huge new datasets to inform advanced machine learning.

8) Full-Stack Engineers: Companies will scramble to hire full-stack engineers with AI and analytics skills making this one of the hottest careers of 2019!

 

Download DMI’s 8 Most Important AI and Analytics Trends for 2019 e-Book here.

About DMI DMI, a leading end-to-end mobility company, combines all the skills and services necessary to deliver mobile enterprise solutions. Built to reinvent business for the connected world, DMI has expertise in enterprise-strength web and app development, IoT, digital commerce, analytics, brand and marketing, and secure device and app management. The company’s unique, integrated approach to mobility has resulted in dramatic growth as well as an expanding client base, which includes hundreds of enterprise commercial clients and all fifteen U.S. Federal Departments. Additional information is available at www.dminc.com and on LinkedIn, Twitter, Facebook, and Google+.

Contact:Donna SavareseSr. Director, Media Relations This email address is being protected from spambots. You need JavaScript enabled to view it.(240) 720-0414

Open your Facebook feed, a newspaper or turn on the news and you’ll likely see something about the dangers of machine learning, the increasing amount of fake news or even the dangers of AI on our privacy. Yet, these technologies are continuing to develop and thanks to new developments in automation and machine deception – they will continue to shape the use of AI over the coming year. 

1.       New technologies will enable partial automation of tasks

Automation occurs in stages. While full automation might still be a way off, there are many workflows and tasks that lend themselves to partial automation. In fact, McKinsey estimates that “fewer than 5 per cent of occupations can be entirely automated using current technology. However, about 60 per cent of occupations could have 30 per cent or more of their constituent activities automated.”

We have already seen some interesting products and services that rely on computer vision and speech technologies, and we expect to see even more in 2019. Look for additional improvements in language models and robotics that will result in solutions that target text and physical tasks. Rather than waiting for a complete automation model, competition will drive organisations to implement partial automation solutions and the success of those partial automation projects will spur further development.

2.       Artificial Intelligence in the enterprise will build upon existing analytic applications

Companies have spent the last few years building processes and infrastructure to unlock disparate data sources in order to improve analytics on their most mission-critical analysis, whether it is business analytics, recommenders and personalisation, forecasting, or anomaly detection and monitoring.

Aside from new systems that use vision and speech technologies, we expect early forays into deep learning and reinforcement learning will be in areas where companies already have data and machine learning in place. For example, companies are infusing their systems for temporal and geospatial data with deep learning, resulting in scalable and more accurate hybrid systems (i.e., systems that combine deep learning with other machine learning methods).

3.       UX/UI design will become critical

Many current AI solutions work hand in hand with consumers, human workers, and domain experts. These systems improve the productivity of users and in many cases enable them to perform tasks at incredible scale and accuracy. Proper UX/UI design not only streamlines those tasks but also goes a long way toward getting users to trust and use AI solutions.

4.       Hardware will become more specialised for sensing, model training, and model inference

The resurgence in deep learning began around 2011 with record-setting models in speech and computer vision. Today, there is certainly enough scale to justify specialised hardware--Facebook alone makes trillions of predictions per day. Google has also had enough scale to justify producing its own specialised hardware. It has been using tensor processing units (TPUs) un its cloud since last year.  Therefore, 2019 should see a broader selection of specialised hardware begin to appear. Numerous companies and startups in China and the US have been working on hardware that targets model building and inference, both in the data centre and on edge devices.

5.       Hybrid models will remain important

While deep learning continues to drive a lot of interesting research, most end-to-end solutions are hybrid systems. In 2019, we’ll begin to hear more about the essential role of other components and methods including model-based methods like Bayesian inference, tree search, evolution, knowledge graphs, simulation platforms, and many more. And we just might begin to see exciting developments in machine learning methods that aren’t based on neural networks

6.       Investments will be made into new tools and processes

We are in a highly empirical era for machine learning. Tools for ML development will need to account for the importance of data, experimentation and model search, and model deployment and monitoring. Take just one step of the process: model building. Companies are beginning to look into tools for data lineage, metadata management and analysis, efficient utilisation of compute resources, efficient model search and hyperparameter tuning.  In 2019, we can expect many new tools to ease the development and actual deployment of AI and Ml to products and services.

7.       Challenges around machine deception will increase

In spite of a barrage of “fake” news, we’re still in the early days of machine-generated content (fake images, video, audio, and text). At least for now, detection and forensic technologies have been able to ferret out fake video and images. But the tools for generating fake content are improving quickly so we must ensure that detection technologies are able to keep pace.

Machine deception does not just refer to machines deceiving humans however. It also refers to machines deceiving machines (bots) and people deceiving machines (troll armies and click farms). Information propagation methods and click farms will continue to be used to fool ranking systems on content and retail platforms, and methods to detect and combat this will have to be developed as fast as new forms of machine deception are launched.

8.       Questions will be raised around reliability and safety

It’s been heartening to see researchers and practitioners become seriously interested and engaged in issues pertaining to privacy, fairness, and ethics. But as AI systems become deployed in mission-critical applications including life and death scenarios, improved efficiency from automation will need to come with safety and reliability measurements and guarantees. The rise of machine deception in online platforms, as well as recent accidents involving autonomous vehicles, has cracked this issue wide open. In 2019, we can expect to hear safety discussed more intensively.

9.       Access to more data will help companies to take advantage of data they didn’t generate

Because many of the models we rely on, including deep learning and reinforcement learning are data hungry, the anticipated winners in the field of AI have been huge companies or countries with access to massive amounts of data. But services for generating labelled datasets are beginning to use machine learning tools to help their human workers scale and improve their accuracy. And in certain domains, new tools like generative adversarial networks (GAN) and simulation platforms are able to provide realistic synthetic data, which can be used to train machine learning models. Thanks to new and secure privacy preserving technologies, organisations can take advantage of data they didn’t create themselves. Consequently, smaller organisations will gain the ability to compete by using machine learning and AI.

Ben Lorica, Chief Data Scientist, O'Reilly Media
Image Credit: John Williams RUS / Shutterstock

Ben Lorica, Chief Data Scientist at O'Reilly Media, Inc. and Programme Director of both the Strata Data Conference and the Artificial Intelligence Conference takes a look at the trends that will shape AI in 2019.

Read Source Article ITProPortal

#AI #trends #DataScientist #Conference #blog #Article

Who's afraid of robots? Here's how to stay one step ahead of the competition

Could a robot do your job? Could you help a robot do its job? If you are thinking about your career development and where you’d like to be a year from now, it’s time to ask yourself these questions. IDC estimates that 40 percent of digital transformation initiatives in 2019 will use AI services, and by 2021, 75 percent of enterprise applications will use AI. No matter your title – from entry level to CIO – it’s wise to think about how your role and responsibilities may shift as technologies like AI, automation, and robotics evolve and get smarter.

Constant learning is key to staying one step ahead of the robots, says Jim Johnson, senior vice president at Robert Half Technology.

“The tech industry moves quickly, but keeping up – or staying one step ahead – of the latest tech advancements is a good way to future-proof your career prospects,” says Johnson.

Moreover, demonstrate your creativity, innovation, and drive to learn, says Johnson: These qualities, which robots haven’t quite mastered yet, make you an indispensable member of the team.

Here are five other ideas for future-proofing your job in the age of AI.

1. Show your value in new ways

Sammy Migues, principal scientist, Synopsys: "If your job today is doing something algorithmic and reproducible in IT, your role will eventually go away. It’s just a matter of time. Of course, as technology evolves, so will the interactions between humans and automation. This will require new skills. Forward-looking IT professionals need to take a hard look at their place in the IT value stream and give some thought to whether a computer can do their job. Those who didn’t do this in industries such as legal, medical, hospitality, food service, manufacturing, and so on suddenly found themselves displaced and without a plan.

So, what can IT professionals do now?

  • Take a look at what you do and how you do it. Be the person who suggests automation improvements. If you’re the person who can make processes more efficient, then you provide a necessary value to the organization.
  • Learn to make the automation work. You’re a firewall wizard today? Become a container and orchestration wizard. Then get on the team that is rolling out all that automation and maintaining it.
  • Learn to determine if the automation is doing its job correctly. Learn a scripting language and be able to write tests and sensors that determine whether the automation is meeting expectations.
  • Add a skill, such as security, to your IT skill set. Expand your role as a developer into that of a security-proficient developer. Go from a cloud engineer to a cloud security engineer. Grow from a network engineer into a cloud security architecture engineer. And so on.
  • Learn to use tools beyond your current niche. There are lots of free cloud tools, security tools, and so on that require a smart person to conduct manual analysis in order for the tool to be effective. That manual part will always be manual. Your current firm might even offer this training internally for free."

2. Be proactive, rather than reactive

Ian Pitt, CIO, LogMeIn: "The rise of AI within the IT space will not replace the entire IT team overnight, nor will it get close any time soon due to the current applications of the technology. As AI starts to erode the need for humans in the IT helpdesk, those that wish to survive in IT need to do what they need to do anyway – grow, expand into higher value areas, and maintain a close relationship with the business.

One small group of users with a loud voice should not undermine a longer-term strategy of integration and overall company productivity.

IT decision makers must have a seat at the table – get ahead of the business, learn what’s trending and productive in the marketplace and stay connected to the new-to-the-workforce team members. They need to move away from the ‘we’ve always done it this way’ mentality but also stand their ground when challenged to adopt a new solution simply because a user or team has decided that’s what they want without considering existing applications.

Keep in mind that in a large organization, one small group of users with a loud voice should not undermine a longer-term strategy of integration and overall company productivity. Leaders need to bring solutions to the senior management and transition into a trusted advisor/consultant role; otherwise, they will forever be reactive. Failing to evolve into this strategic leadership position will lead to an IT decision maker's extinction."

3. Adaptability is the new power skill

Tim Mackey, technical evangelist, Black Duck by Synopsys: "IT professionals have long experienced the disruptive forces of automation and technology transformation. From x86 server farms, through virtualization and now into containerized applications, we can see that those IT professionals who were adaptable and able to embrace new paradigms succeeded. With AI and machine learning being one of the next frontiers, those same soft skills will serve the IT community well. Couple that with an understanding that at the heart of AI/ML is data and that data management and distributed systems design play key roles in any data-driven system, the skill sets most in demand in a data economy will be those with an ability to manage, interpret, design, secure, and support large-scale data operations."

4. Learn a new language

Jess Bracht, instructor, Fullstack Academy: "For IT professionals, learning to code is probably the smartest step you can take toward future-proofing your career because ultimately software developers are the ones writing the programs that end up replacing IT professionals.

Many help desk specialists are being replaced by automated chatbots. But who are the people behind the scenes creating those chatbots?

For example, many help desk specialists are being replaced by automated chatbots. But who are the people behind the scenes creating those chatbots? Software developers. And they’re using really cool tech like AI, NLP (neuro-linguistic programming), and probably some machine learning in there as well. So you’re future-proofing your career and increasing your desirability (and value) as an employee by concentrating on those really hot fields.

Anyone who’s already a developer can focus on those same fields to move ahead in their own career. A web developer, for example, can study basic statistics if they want to get into machine learning and add a language like Python, commonly used in the machine learning field, to their repertoire."

5. Do what robots can't: Think outside the box

Jim Johnson, senior vice president at Robert Half Technology: "It’s true that a majority of jobs will likely change from the continuous integration, evolution, and introduction of new AI technologies, and many for the better. AI may be able to take care of routine tasks and provide assistance for many IT projects, but they won’t be able to accomplish tasks that require soft skills, like intuition, critical thinking, creativity, and empathy, to name a few.

That’s why it’s crucial that professionals develop these interpersonal skills. To do so, find out what soft skills you need to work on. Ask a trusted colleague or your manager to give you constructive feedback on your leadership skills, how well you work on a team or with customers, how you communicate, etc. You can then find professional courses, consult with a mentor, or take on new responsibilities to develop these skills. These actions will help you future-proof your career, as well as show your manager your dedication to professional development.

Additionally, and equally as important, you need to make sure you understand your business. How is your organization structured and how do your efforts help the bottom-line? Do you know all your company’s products and services? Knowing the ins and outs of your business will help you best serve and respond to your customers, something a robot can’t do as seamlessly. If you want to better understand your business, consider asking colleagues in other departments for coffee to learn more about their roles and teams’ goals."

Read Source Article by Carla Rudder is a writer and content manager on The Enterprisers Project.

#AI #Technology #Robotics #futureofAI #IT

Oxford uni gets award of cash to study AI and law, Qualcomm invests in startups, etc

AI and law: The University of Oxford has received £1.2m to study the different ways that AI might revamp the legal industry.

The project titled “Unlocking the Potential of AI for English Law” has been funded by the Economic and Social Research Council (ESRC), part of UK Research and Innovation, an organisation supported by the British government.

Algorithms are already being used in law, such as assessing the risk of people recommiting crimes. The data used to support such claims is often biased, and researchers have warned against its possibly harmful effects.

That’s not the only way algorithms can be used, however. Researchers from computer science, law, economics and social policy backgrounds will be looking at other applications too, including using computers for legal reasoning in criminal and civil lawsuits, training lawyers to have technical expertise and working out the best ways AI can be applied ethically and morally.

“The project team will draw on relevant expertise from a wide range of disciplines across the University, and we will work together with a number of private sector partners who are also engaging with these issues,” said John Armour, a law professor from Oxford University, leading the project.

Qualcomm’s AI investment fund: Qualcomm has put aside $100m to invest in AI startups.

The Qualcomm Ventures AI Fund is particularly interested in practical applications like autonomous cars, robotics and machine learning platforms. So far, it has funded AnyVision, a computer vision startup based in Israel which is carrying out image recognition tasks on devices, in a Series A funding round.

“Qualcomm Ventures is proud to invest in the future of AnyVision and many other key players in the AI industry,” said Quinn Li, senior veep of Ventures, Qualcomm Technologies and global head of Qualcomm Ventures. “This investment builds on our long history of successful AI investments, including Cruise Automation, Brain Corp, Clarifai, Prospera, SenseTime and Retail Next.”

Now you can buy AI art too: Are you a fan of strange artwork created by generative adversarial networks? Well if you are, you can hang a portrait in your living room.

Ganvas Studio is run by Danielle Baskin, a painter in San Francisco is exploring GANs as art. People can buy pre-designed prints or submit a picture of their choice, where it’ll be fed to BigGAN, a model created by researchers from DeepMind and Heriot-Watt University.

Different images are then ‘bred’ using GANbreeder, a tool that mixes up different pictures to create artwork that looks familiar but unsettling. Think seat belts and plates of foodor jellyfish and comic books.

The pictures aren’t the best quality, so Ganvas Studio will help accentuate some details to avoid the prints looking too pixelated with real paint. How much this will cost has yet to be revealed.

AI and pop music: No, this isn’t about machines making pop music. It’s about humans making pop music about machines.

Grimes, known for her goth alien princess aesthetic, has gone all cyberpunk. She has had a long fascination with AI and bonded over the idea of Roko basilisk with her tech billionaire boyfriend Elon Musk.

Roko Basilisk describes a dangerous situation that could arise with the creation of general artificial intelligence. As the powerful entity comes to life, it might punish those that did not help bring it to life sooner.

It seems that Grimes’ new song “We Appreciate Power” is written to avert such dangers. In the music video, she’s seen holding an assortment of weapons or posing with her friend and collaborator Hana in skintight rubber suits. The lyrics really are something else.

In her high pitched breathy voice she sings: “People like to say that we're insane. But AI will reward us when it reigns. Pledge allegiance to the world's most powerful computer. Simulation: it's the future.”

She also tells us to upload our mind somehow, and that we’re not even alive since we’re not backed up on a drive. Her last parting words: “Neanderthal to human being. Evolution, kill the gene. Biology is superficial. Intelligence is artificial. Submit, submit, submit, submit, submit, submit, submit.”

Read the source article The Register

#ML #Qualcomm #Oxford

 

Blockchain technology is so synonymous with cryptocurrencies, and especially Bitcoin that it is almost like the financial sector has usurped its potential. In times like these, where an investing bear market has befallen the cryptocurrency space, it is easy to get down on the revolutionary possibilities of blockchain technology.

Since the original Blockchain, that is Bitcoin, emerged, there has been a considerable focus on transactional blockchains which have been at the forefront of the mainstream understanding of the technology. Bitcoin is often the layman’s first point of call with the stories of investing success stories obscuring the view of other possibilities.

However, blockchain technology is moving along in an undercurrent separate from the comings and goings of the cryptocurrency market and the financial interest it has garnered in just a few short years.

Ethereum and smart contracts have taken blockchain technology to a second generation where many different sectors are in the sights of its potential disruption. Even a third generation is being bandied about, with regards to Directed Acylic Graphs, but the fourth generation - which will be an essential part of the fourth industrial revolution - will need the help of some similar revolutionary technology.  

Artificial Intelligence (AI) has been cutting a distinct but similar path through the nascent stage of technology development. Its uses and adoption have been growing, and its implementation has reached a critical point.

Read complete source article in FORBES
 
#MachineLearning #ArtificialIntelligence #Technology

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