Monthly Archives: April 2017

So far, it's safe to say that the predominant trend in 21st-century business has been digitalization. Every industry, organization and individual has been touched by it one way or another.

As we head towards 2020, we are moving to the next level of digitalization. Now, what has already been digitalized will increasingly be automated -- whether it's the way we work, trade or connect with each other. Automation is becoming increasingly prevalent as computers gain in processing speed and power, and as the amount of data available for computation continues to grow exponentially. At the start of the Internetage, very few things were connected and available for analysis. But with the rise of the Internet of Things and the implantation of computers into all walks of life, from driving to warehousing, more and more facets of our world can now be mapped from within dedicated software.

As a result, a whole range of new abilities and efficiencies are becoming available to companies in all kinds of industry. Automated analyses and tasks can provide actionable insights that previously would have taken a human hours or even days to produce. Whats more, handing over mundane tasks to computers can free up humans to focus on more creative tasks such as strategy and planning.

In light of this, Exact has put together some predictions for the years ahead. Weve all heard the horror predictions that automation will eventually take over the human job market, but in reality it has the potential to be a highly effective tool if used in the right way. Here are some of the key areas of business that will benefit.

Automation of trust: Blockchain

Financial transactions are the core of our global economic system. A lot of effort goes into making sure that those transactions can be completed in a safe and trusted way. We need central banks to allow money to be transferred from one person or organization to the other, with every piece of stock that is traded registered by a Central Security Depository (CSD).

Or at least, we did until recently. Over the last year the digital currency bitcoin has showed us that it is possible to completely decentralise trust. When money is transferred from one person to the other, the transfer is registered in a central administration, and every participant in the network has a copy of the data. As such, Bitcoin automates the trust of a financial transaction.

Blockchain, the technology behind Bitcoin, has the potential to disrupt many more elements of our economy and society. In the accounting industry, for example, working capital financing and triple-entry accounting are very tangible applications of blockchain technology. For working capital financing, blockchain technology can combine and register data that is locked inside the systems of companies and organizations. This increases the value of information, leading to an increase in security and a lower risk level, making financing easier and cheaper. New financing solutions then become available as a result.

In triple-entry accounting, traditional auditing will also change because of blockchain. The reputation of customers, based on historical and actual payment data, will be available in blockchain. Also, transactions between organisations will be matched via this technology in real-time, simplifying the traditional annual audit or even making it redundant.

The finance world is traditionally slow to change, as its important to make sure new technology is secure. With blockchain, that concern is covered, and as such, financial companies are in a good position to benefit from automation in this area.

Automation of manufacturing processes: the IoT

2017 will see increased access to real-time production and order data, allowing for direct orchestration of activity rates on the assembly line. Coupled with increased plant automation enabled by the industrial Internet of Things (IoT), machinery will become ever less reliant on direct human intervention, allowing for more centralized management and control. This will also support more ad-hoc production runs, allowing manufacturers to be more agile and reflective of customer demand and current stock levels in the supply chain.

Automation of personalization: machine learning

In the old days, most consumers would buy their meat at their regular butcher and bread at the baker at the corner. These retailers knew our specific tastes well. This allowed them to optimize customer experience by suggesting new products based on our preferences, or even presenting a tailored, personal offer. The rise of supermarkets and shopping malls made retailing less personal; we could no longer rely on a befriended shop owner.

As contradictory as it may sound, e-commerce holds the promise to bring back that personal flavor in customer experience. Thanks to machine learning, Netflix can predict the kind of series that you like and Spotify offers a weekly personalized playlist with suggested songs based on your historical preferences. Machine learning allows computers to discover patterns based on big data; thanks to these algorithms the services they offer get better and more personal.

This is not only applied in e-commerce, but also in financial and business software. For instance, machine learning can recognise bank statements and automatically book the correct general ledger account as a result.

Automation of financial processes: standardization

In 2017 we expect to see more rationalization of financial and accounting red tape, with a strong shift towards electronic filing and standardized data. Electronic transactions move more of a companys financial dealings onto a fully digital, real-time basis. Since the output is standardized, the source must be too. For example, e-invoices will already be coded in line with the output requirements for filing.

This will necessitate changes to accounting, book keeping and banking processes, but in return will provide much more clarity and certainty of cash flow, financial health and trading outcomes. It will also lower operating costs by further reducing cash handling for many organizations. The digitalization of financial processes also paves the way for a shift towards real-time banking --financial transactions can now be processed instantaneously. This allows companies to have constantly up-to-date insights into their financial balance.

Gavin Fell, general manager UK, Exact

Published under license from ITProPortal.com, a Future plc Publication. All rights reserved.

Photo Credit: Wright Studio/Shutterstock

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Automation is the next level of digitization - BetaNews

St Pauls in Adelaide has changed plenty over the years. The bluestone facade, lancet windows and sloping roof of this 19th century Anglican chapel suggest yet another house of worship in Australias city of churches but it hasnt been that for over 30 years.

In the decades since God left the building, St Pauls has been converted into a secular community centre, a hedonistic nightclub, and lately trades as a South Australian government-backed co-working space.

Yet, those seeking salvation are undeterred. The growing number of homeless people left behind by Adelaides rapidly de-industrialising economy still occasionally wander inside, thinking it a place of sanctuary.

Theyd be right, just not in the sense they were expecting.

Within the sermon hall and the labyrinth of corridors beneath, developers stagger around with virtual reality headsets wrapped around their faces, designers and engineers hunch over 3D printers, musicians hone their digital marketing strategies, and tech heads lounge in leather bound chairs debating the internet of things beneath stained glass windows.

Within St Pauls Creative Centre these people have found a refuge from the economic headwinds pummelling Adelaides closing car factories, shuttered retail businesses and abandoned offices.

If I dont know how to code, Im going to be screwed in the next few years.

Many of the buildings new congregation are working in industries or with technologies that didnt exist a couple of years ago, such as Ben Tripodi, the managing director of MIK Health. Tripodi boasts a rather hectic CV that encompasses work with mental health, digital marketing, cutting-edge bike part design, nutrition, development of high-tech sports garments, not to mention an athletic career that inspired most of his bright ideas. He has co-founded three businesses in three different sectors, and won a medley of awards in recognition of his innovative capacity. Despite all that, Tripodi fears being left behind.

If I dont know how to code, Im going to be screwed in the next few years, he says.

It is nothing remarkable that someone with so much professional experience is reskilling, but Tripodi is not some middle-aged veteran trying to keep up with the new wave of talent at 24 years of age, he is the new talent.

It was only 2015 when Tripodi graduated from his bachelor of health sciences at Flinders University the last couple of years part-time so he could tend to his business ventures and since then he has completed four more courses online and developed new professional skills from conferences, networking and a digital mountain of books queued up on his Kindle.

Tripodi might teeter at the leading edge of several emerging industries but, like many of those working around him at St Pauls, he is always looking to leap to the next rising sector as the ones behind him fall away: a work model that futurists predict will become the norm for us all as technology advancements continue to accelerate. Welcome to the fourth industrial revolution: the economy of always learning.

Staying still is more likely than ever to result in obsolescence, as indicated by a report released last month by consultancy firm PwC, which estimated 30% of British jobs could be automated by 2030.

As professionals need to update their skills more frequently than ever, so too the education sector is evolving to cater to a new state of affairs in which young people are projected to have 17 jobs over five different careers, according to the Foundation for Young Australians 2015 report, The New Work Order.

That doesnt mean existing models of education are no longer relevant Tripodi credits his degree in health science with teaching him not just the skills of that particular sector, but the kind of capabilities that will enable him to continue to learn and develop on his own. Studying at a forward-thinking education body such as Flinders University meant he was able to take advantage of additional learning opportunities beyond a mere degree. These included the universitys New Venture Institute that linked him up with other aspiring entrepreneurs, provided him with mentoring by established businesspeople and opened up access to engineering and office facilities. It was at NVI that he established his bike component business, seeking to engage companies that had excess capacity after losing work in Adelaides declining car industry.

After finishing his degree, Tripodi is still working with Flinders University on new projects. This includes his current work with MIK Health, which is developing machine learning processes that undertake sentiment analysis of workers to confidentially assess their mental health, and allows companies to ensure they arent putting too much demand on their workforce.

To deliver this, Tripodi is drawing on his existing health and business skillset and making the most of the tech-proficient people hes met through NVI and St Pauls, the latter of which he joined through working for digital agency Made In Katana, the majority owner of his health venture.

Our generation is really pro-mental health, and I think mental health is ripe to be disrupted, no one is really building anything like this, he says.

If he was relying on the skill set he obtained out of university alone, Tripodis new venture would have been hard for him to imagine, but the online courses he completed after graduating have granted new skills. He is currently studying coding so he can communicate better with the technical side of his team, and says the ability to learn online suits his busy lifestyle.

It is convenient, because you can do it in your own time, he says.

Its there for you, you dont have to rock up to a classroom anywhere, you dont need to wait for someone to teach you, its there whenever you want to access it, 24/7. Its the most convenient learning youll ever get, its just whether you can motivate yourself to do it. Universities are going to start to change their approach at the end of the day people are rushed and want to learn more quickly in a quick amount of time.

Susan Drew, the senior regional director at Hays recruitment, says the projected impacts of the fourth industrial revolution are already being realised. The agency has noticed workers changing roles increasingly rapidly, with many in the manufacturing and finance sectors losing their roles to automation and outsourcing.

She says it is not all forced, however, with Gen Y workers preferring to move around, and employers seeking people with more diverse experiences. The organisations are more accepting of the fact they are getting real value from someone in a very different industry One area in particular is we are seeing not-for-profits looking for candidates with an industry focus.

Drew says a university degree remains important, but of increasing value is a CV brimming with examples of lifelong learning. Candidates have to be involved in professional development, she says.

It is easier than ever for people to demonstrate that want and desire to continually develop by online training, but it doesnt have to be formal networking, being part of groups on social media, following certain people on Ted Talks, broadening day to day knowledge anything to demonstrate that want and desire to improve and develop to be at the forefront of technology.

What is clear is that a generation used to on-demand media content services such as Youtube and Netflix are increasingly wanting the same from their education services binge-learning new skills just as they binge-watch their favourite shows, rather than waiting for a university degree to dole out information the way a TV station drip-feeds content to a set schedule.

Online learning has been a staple of the internet since it first blinked into existence, but from corporations such as Microsoft offering up courses that teach the skills they want potential job candidates to have, to Apple rolling out educational offerings on iTunes, today the options are almost endless.

Some millennials have taken it upon themselves to create formalised education systems they feel best suit the needs of their world. A case in point is 30-year-old entrepreneur Adam Brimo, the co-founder and chief executive of Australian startup Openlearning, one of the medley of platforms hosting Moocs (massive open online courses) that have exploded in popularity over the past half-decade.

OpenLearning itself has grown from 150,000 to 750,000 enrolments over the past two years, and from 500 to over 3,000 courses in the same timeframe. Courses can involve video lectures, interaction with teachers and online readings, often for free or at a fraction of the cost of a traditional degree.

Brimo says the most popular subjects are entrepreneurship, cybersecurity, teacher training and creative arts.

OpenLearning provides the platform for educators to upload their courses, as well as expert advice on how to effectively design an online learning tool, the necessary documentation and online customer service. Many people today are looking to learn new skills, however they dont have the time or patience to complete an entire qualification. Therefore, they are looking for courses that can be completed in weeks or months, rather than years, he says.

Our learning designers are experienced teachers who help educators convert their face-to-face course materials into engaging, active online learning experiences for students.

Openlearning has had particular success in Malaysia, where last year it assisted the national government in rolling out the worlds first national guidelines on credit transfer for Moocs to ensure such courses were as valuable as a conventional education.

A raft of universities now allow Mooc course credits to count towards their final degree, with the UKs Open University and the University of Leeds two of the latest examples to make the leap.

Free study for students is obviously appealing, but how are they economically sustainable for the provider? For Openlearning, the answer is a model thats free to study, but students pay for the certification.

To help his system flourish, Brimo would like to see governments update how they support students.

Most government funding and support schemes for higher education are centred around entire qualifications, thereby allowing students to complete entire degrees without paying any money upfront, he says.

While this is great, it has led to universities and education providers charging upfront fees for individual courses. Therefore, the immediate out-of-pocket costs of a single course are higher than for an entire university degree. Government should consider whether they should provide support or funding for individual, accredited courses in addition to entire degrees.

Openlearning is just one of a plethora of Mooc platforms, with the global leaders including Udemy, FutureLearn and Coursera. Mooc aggregator Class Central last year estimated over 500 universities offer such courses for an estimated 35 million students.

At first, leading academic institutions saw Moocs as a kind of taster to lure students into a full degree, but today many also see them as a tool to help continue relationships with alumni after theyve graduated.

One of the platforms preferred by elite universities is edX, founded by Harvard University and MIT in 2012, which today has grown to include courses from 110 different education providers, including as of last year Oxford University.

The University of Queensland is also on board, having had 1.5m registrations since the institutions first Mooc was uploaded onto edX in 2014.

UQx director John Zornig says the boom in popularity of online learning is reflective of a changing work environment: Having been in the IT industry most of my career, I appreciate the need to keep learning if you want to stay relevant and have access to good employment opportunities.

Today, Moocs are impacting the post-university workforce more than they are pre-university students. I see it everyday in the demographics of our Mooc learners. The fact that the courses are accessible for free means that the barrier to having a look is very low.

He says they even keep old versions of courses available in online archives, with learners still using content that was replaced in 2014.

What I tell students is that they have two options after university: apply for a job, or create their own.

They have integrated those courses into their personal library of knowledge and now dip into it whenever they need to check the accuracy of their memory or to look for an answer to a problem they are facing, he says. This is very different to the common practice of just googling for info. Many Moocs are also forming lasting social networks among learners who connect around the subject matter.

The University of Queensland or UQ as it prefers to be branded these days is not just updating its online presence to better cater to the needs of the modern economy: it is also scrambling to reshape its in-person options.

Nimrod Klayman is the director of the UQ Idea Hub, a startup incubator that seeks to kickstart the careers of entrepreneurs in a similar fashion to how the New Venture Institute launched Tripodis career in Adelaide. He says the hub has attracted registrations from high school students right through to UQ alumni already working in the corporate world.

The program is structured around workshops and Q&A sessions with successful entrepreneurs, cultivating businesses in areas as diverse as drone development, distilleries, food science, automation, financial technology and safety equipment.

What I tell students is that they have two options after university: apply for a job, or create their own, he says. We leave students to work on their idea, they need to present progress each year, go do market validation, find customers, and present a minimum viable product ready for sale.

Universities have always been in a state of evolution the last century alone seeing a transformation of once-stuffy intellectual fortresses for the upper classes into thrumming hotbeds of activism, free thought and formative life experiences, before straightening out into todays professional job-factories. Are they now on a path to becoming providers of online self-help courses that occasionally host on-campus pep talks from corporate successes?

Anne Knock, the director of development at the Sydney Centre for Innovation in Learning, thinks there are certain fundamentals that education institutions will need to retain as they evolve. Moocs havent been the great salvation everyone hyped them to be education is predominantly a relationship activity, she says.

According to Knock, online education is useful for surface learning, which provides the tools to delve into deep learning. What Im finding with universities is because students can access coursework online, theyve discovered places for social learning are equally important, she says.

The question is how do we create an environment where students want to come to campus you cant negate the fact that face-to-face learning is a social activity.

She notes that lifelong learning has never been so important as now, with the projected redundancies from automation in particular requiring a change in focus in what is taught.

What we really need to be focusing on is a sense of being creative in our learning, not locking into one job for the rest of your life. You need a range of skills, she says, pausing for a moment.

The job for life era has truly past.

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Seventeen jobs, five careers: learning in the age of automation - The Guardian

Key Takeaways

In todays world of disruptive technology innovation, needless to say that Lean Principles apply to any field of IT, and as we will see now, Lean Principles also apply to more than just manual processes in IT environment.

About Ericsson: Ericsson is a global leader in delivering ICT solutions, carrying over 40% of the world's mobile traffic through its networks. It has customers in over 180 countries and comprehensive industry solutions ranging from Cloud services and Mobile Broadband to Network Design and Optimization.

In our service delivery unit IT & Cloud (SDU IT&C), we commenced the Lean Journey with small steps around five years ago. We selected a few important KPIs aligned with the organizations strategy and initiated lean transformation programs on those areas which helped us by delivering consistently on the following parameters:

After successfully completing these lean programs, we focused on implementing the improvement levers across the organization to maximize benefits.

However, in recent times our management had been receiving feedback from stakeholders and customers that we need to further lower cost and reduce cycle time in order to remain competitive and win more business. Their points of concerns included:

At this critical stage we initiated the Lean Automation Program with the aim of improving delivery speed, quality and efficiency through automating repetitive and effort intensive tasks performed in different customer projects across the organization.

By commencing the Lean Automation Program, our sponsor set the following expectations clearly for the organization to deliver:

We will take a deeper look into how these goals were evolved in the next section when we discuss the Voice of Customer to Critical to Quality exercise for this program.

As per our Lean Deployment Framework (ref. following diagram), we first defined a charter for aligning with the sponsors expectation and started conducting our workshops across locations through virtual collaboration with the deployment team which consisted of the program driver, lean coaches, automation SPOCs as nominated from each delivery unit, development lead, and developers (as needed from shared resource pool). This exercise is referred to as workshop 0 in Current State Assessment.

Fig1: Deployment Road-map - Current State Assessment of Value Stream

In the following workshops (workshop1), the deployment team championed a multitude of activities, some of which are:

Fig2: Cause-n-Effect Analysis (Fish Bone Analysis followed by Pareto) to find out vital few top probable causes

Fig 3: Table Initial Voice of Customer to CTQ analysis to Identify Primary KPI to Improve Upon

Thereafter, during workshop 2, the team conducted multiple interviews with different stakeholders for all process areas and came up with a detailed value stream map of the end-to-end process. Through these exhaustive workshops, the team acquired a comprehensive understanding of the process, analyzed wastes and earmarked improvement opportunities in repetitive manual tasks or to address high defect rates.

Fig4A: A sample current state value stream map on a typical system integration/installation/upgrade project

Fig4B: A sample current state value stream map on a typical testing cycle

The team further collected data on each of the activities and then prioritized them in order to identify the top contributing areas in manual repetitive tasks and high defect percentage.

The significant inferences that were summarized as outcomes of the current state assessment workshops are:

Fig5: Analysis of activities from different processes to identify top-most contributing factors for high lead time and high defect percentage

After the data was collected and baselined, the VoC to CTQ exercise was updated to have the baselined KPIs with improvement targets for the same. Please refer to the following tables on baselined data and revised CTQ with improvement target:

Fig 6: Table Modified VoC to CTQ with baselined KPI and Improvement Target

Fig6: Deployment Road-map Designing the Future State and Implement Solutions

During workshop 3, the lean automation team designed the future state of the value stream, eliminating wastes, addressing probable causes and bridging gaps through short term and long term solution levers.

Finally, as a part of workshop 4, the team came up with innovative ideas for improvement through different techniques (namely - six thinking hats, affinity diagrams, and blue sky), translated them into practical solutions with proper definitions and then prioritized them for implementation with defined measurement criteria.

Key steps followed:

Fig7: Solution Selection Matrix to prioritize and select most impactful solution levers for implementation

Fig8: Important Solution Levers across technology areas that were developed through the Lean Automation Program

Fig9: Exercise on pilot implementation to identify possible occurrences in projects and projected efficiency gain against baseline

Fig10: Designing Improved Process State: Proposed Lean Automation Process Flow: setting up a continuous process of PDCA cycles

Benefits from this program have been quite significant so far. Pilot Implementation results that were reported include:

Fig11: Month-on-month savings of effort (hrs.) through pilot implementation

Fig12: Maximizing program benefit with sustenance plan through continuous PDCA cycles

While there was an imminent benefit in saving redundant manual efforts and increased delivery quality and efficiency, all of us in the organization could experience the changes in some of the cultural aspects, namely:

In some way or another, all of us experienced challenges and resistance to any change from the status quo. And it is never easy to bring in changes to overcome such resistances. In general, the resistances to lean automation may be perceived under two categories: cost and people. And two of the most common benefits are efficiency and effectiveness. Both of these factors are convincing arguments to proceed with lean automation programs.

The companies that implement lean automation early often see positive bottom line results from their efforts. However, cost savings are not the primary reason to automate IT operations. The focus should be on improving service to the clients and end users. As the quality of this service improves with automation, the costs associated with it also improve.

A few learnings our team experienced from our lean automation programs:

Some of the other lessons the team learnt through their journey in the lean deployment program:

With technology and services evolving faster each year, and growing customer demands, mastering the delivery has become the key to success - speed, quality and efficiency being the essential components. Innovation is the critical driver to succeed and survive in this competitive market; however both innovation and technology evolutions have increasingly become disruptive day by day.

According to experts at The Boston Consulting Group (BCG) and Wharton faculty, lean and innovation can indeed complement each other, and its about time they came together. Lean brings structure and predictability to innovation, and sharpens the distinction between idea generation and the development process, they say. Both share a common goal: to meet customer needs in a cost-effective manner. And lean can help empower researchers and reduce uncertainty in the innovation process itself.

While lean principles enable us to be effective and innovative everywhere we work, finding automation opportunities across every technology and customer focused processes can unlock a bigger potential for repeatedly delivering value to customers.

Nevertheless, when implementing lean principles for automating IT operation, it's important not to forget the human element. Any company that is fully automated still has people working there, and as we all know, any lean initiative should focus on that human aspect carefully. Lean is about involving people and using their brain power to bring in further improvements in the system.

No matter how we deliver IT software and services to our clients, the people part of lean are always a key piece of it. People are the key to identifying when something has gone wrong.

With automation, it's easy to forget that IT processes are only as successful as the people behind them. By contrast, the Toyota Production System seeks to maximize the utilization of people.

The goal of lean automation is to accelerate the frequency and impact of experimentation, thus to make more possibilities for disruptive innovations. And we, the IT engineers, must do as much experimentation as possible early in the process, during the lean automation design stage.

Sudip Pal is Head of Lean Implementation & Execution for Ericsson India Global Services, SDU IT&C. With 18+ years of experience, his areas of expertise span across a wide spectrum of IT landscape from System Integration to IT Service Delivery, from IT Advisory Services to Program Management, from Lean in Agile to Automation.He is a recognized Lean Expert by Asian Productivity Organization Japan, Quality Council of India and National Productivity Council India. He has mentored many big Lean deployment programs for some leading global IT companies in last eight years, such as improved cash flow & profitability, increased win rate, sales engagement process, scaled Agile, etc. In the past two years, Sudip has completed several high potential and critical transformation programs in the areas of Agile, Automation and IT Service Delivery Transformation Model. He has also groomed many candidates to become successful Lean Coaches in the industry.

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Automation and Lean: Scaling up the Lean Value Chain - InfoQ.com

(TurboPatent Photo)

Seattle startup TurboPatent has raised $1.45 million in a convertible note as it gets ready to release the second version of some products and expand its documentation automation capabilities.

TurboPatent focuses on corporations and law firms of all sizes, automating taskslike formatting or document preparation, for example, freeing up people to work on more complex, high-value work. The service is designed to cut costs, save time and lead to more accurate patent documentation.

The 25-person company said it has the same impact on the patent industry as CAD (computer-aided drafting) did for engineering, the cloud for IT infrastructure, and robotics for manufacturing.

The latest funding comes from existing institutional, strategic, and founder investors as well as new private investors. The company raised its first outside funding in 2015,a $2 million seed round led byVoyager Capital.

TurboPatent recently released a new automation product, SmartShell, that helps paralegals speed up responding to office actions and reduces human error.

Formerly known as Patent Navigation, TurboPatent boasts an experienced team led by co-founders James Billmaier and Charles Mirho. Billmaier was previously CEO of Melodeo, a cloud-based media platform company that sold to HP in 2010. He also teamed up with Paul Allen in 1999 to launchhome-entertainment technology company Digeo, which was eventually sold in 2009 to ARRIS Group Inc.

Mirho, meanwhile, is a patent law veteran, having worked as a patent counsel at Intel and later as a managing partner of a patent law firm. He also has a computer science degree from Rutgers.

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Seattle startup TurboPatent raises $1.4M to expand patent automation capabilities - GeekWire

Drawing on your phone or computer can be slow and difficult so we created AutoDraw, a new web-based tool that pairs machine learning with drawings created by talented artists to help you draw, wrote Google Creative Labs creative technologist, Dan Motzenbecker, earlierthis week.

AutoDrawis one of Googles artificial intelligence (AI) experiments, working across platformsto let anyone, irrespective of their artistic flair, create something super quick with little more than a scribble. It guesses what youre trying to draw, then lets you pick from a list of previously created pictures. So you cant draw? No worries! is the general idea here.

Above: AutoDraw

First up, AutoDraw isa super fun tool that gets increasingly addictive that much is clear. But whats alsoclear is that the tool is morea display of AI smarts than it is a tool to improve your artwork, because it would be just as easy to embody the exactsame functionality withina text-basedsearch engine. I mean, why bother drawing a crap dolphin with your finger when you could just type in the word dolphin? Because itwouldnt be nearly as much fun, and Google wouldnt get to show off its fancy new toys.

A few days after Google debuted AutoDraw, it revealed some otherresearch its scientists have been carrying out, designed to enablecomputers to generate simple sketches using artificial intelligence (AI). In effect, theytrained a recurrent neural network (RNN) on sketches that real people made, which emanated from an experimental app calledQuick, Draw! that launched last year (again it is really fun).The app tells you to draw things, like a giraffe or a butterfly,and thenit guesses what youve drawn. So what Google is doing is training machines to sketch like real people, with all the line overlaps and crappy squiggles included.

What thishelps demonstrate is the growing crossover between art and algorithms. But does this hint at a future wherehumans have little incentive to be creative at all?

As part of the so-called fourth industrial revolution, millions of jobs will be lost to automation, according to a recentWorld Economic Forum report. The net loss is expected to be as many as five million jobs by 2020, though of course a whole bunch of new jobs will be created, including positions in IT and data science. Jobssuch as manufacturing and production are expected to be heavily affected, while another recent report indicated that more than 100,000 legal jobs will be automated over the next 20 years.

But art art is sacred. Art is an expression of human sentiment and emotion. Computers stand zero chance of consigning human creativity to the history books. Right? Well, maybe. Were already seeing the early signs that art will be disruptedby machine intelligence and automation.

Why bother learning to paint a landscape or pay someone to sketch your newborn when you can download Prisma to your smartphone and transform your snapshots into ultra-realistic pieces of art in seconds?Prisma, for the uninitiated, usesneural networks to analyze each photo and then appliesa style the user selects. And it really is rather good.

Based on deep-learning techniques, we redraw the image from scratch,said Alexey Moiseenkov, Prisma Labs cofounder, in an interview with VentureBeat last year. We analyze tons of photos and get the typical forms and lines, then take a style and draw your picture with those lines in a taken style.

Above: Prisma: Bottle with Prisma effect applied

The point here isnt that these tools are better than human creators. The point is that such tools are pretty good just now, and theyll only get better. If someone can press a couple of buttons to get an instant hand-drawn family portrait, using little more than a DSLR camera, tripod, and a Prisma-style AI image-rendering app, why would they bother employing the services of a professional artist?

Its not beyond possibility that artists and art retailers will one day have to sell their services based on their authenticity 100% hand-painted pictures could becomethe only visible marking that separates human creations from those produced bymachines.

But technologys algorithmic arm stretches far beyond that of photography and artand into other creative realms.

For years, automated web design services such as Wix and Weebly have offerednovicesan easy-to-use web development platform that makes it simple to buildHTML5 sites using drag-and-drop tools rather than code. For basic websiteswithout much deep functionality, such tools work fairly well. But the formulaic, simplistic, template-based approach leavesmuch to be desired, which is why professional designers and developers still manage to eke out a living.

Last June, Wixlaunchedan automated web design service built on artificial intelligence, called Wix ADI. Using data garnered from its existing user base to feed into this new AI offering,the creator basically answers a few questions and provides the platform with cues as to what theme the website should be based on and what category it exists in, and then Wix pulls in relevant photos, words, and layouts based on the business type and location.

Wix ADI isnt just a new website builder it sets a new market standard for web design,said Wix ADI head Nitzan Achsaf at the launch. We have been at the forefront of this market for nearly a decade, and now as one of the leading AI technology providers, we will make website creation accessible and easy for everyone.

Wix promises that no two websites will look the same.

Other similar AI-focused web design platforms have blossomedin recent times and raised significant venture capital funding, including TheGrid, which has been operating its AI smarts for a few years already, and B12, which launched a similar proposition in beta last year with more than $12 million in funding.

The credibility ofDIY web- and app-design tools that promiseto turn noobs into designers and codershas been questioned for years. And now that AI is going the extra mile to remove any further effort from the process, it will only ruffle the naysayers feathers even more. But the usefulness of such tools really depends on what the purpose of the website is. Why pay for a professional designer and developer when you can hit a few buttonsand have a simple, informative, Google-friendly site made with next to no spadework?

Again, the point hereisnt that the machines are now good enough to replace professionals in building fully functional websites and online services. The point is that AI is encroaching further into creative professions and, more importantly, its improving all the time.

Could an algorithm ever be able to produce something as exquisite as Lennon & McCartney, Jagger & Richards, or even Mozart? Maybe. But probably not, at least for a while.

Back in September, headlines across the web screamedthat the first AI-written pop song had been made. It made for alluring headlines, but it wasnt strictly true. Sony researchers, using specialist Flow Machines software, were able to train a system on different music styles using a gargantuan database of songs. Then combiningstyle transfer, optimization and interaction techniques, the system is able to compose music in any style.

So what we have here is a song called Daddys Car,written in the style of The Beatles. And hey, its not too bad.

However, a more accurate description of this composition would be that it was AI-assisted. French composer Benoit Carr wrote the lyrics (which are pretty nonsensical) and arrangedthe song all the computer did was identify commonalities across this style of pop music and providedCarr with the parts to play around with. Sonys researchers have actually been working on AI-assisted music creationsfor a few years already, and anentire album of suchmusic is expectedlater this year.

Sony isnt the only company dabbling in this field.Last year,Google announcedMagenta, a project from the Google Brain team thats setting out to discover whethermachine learning can create compelling art and music. And earlier this year,the internet giant released aworking interactive version of AI Duet, an app that lets you play a virtual piano with accompaniment from a computer system thatriffs offwhat you play.

Elsewhere, London-based startup Jukedeck is working on an AI-poweredmusic composer that writes original musiccompletely on its own volition. Aimed at video creators on the hunt for original background music, Jukedeck has beentraining deep neural networks to understand how to compose and adapt music,with the end-user able to customize the sound theyre looking for.

All the guitar bands, DJs, and orchestras of the world can perhaps rest easy for now. While computers will improve at songwriting, artists biggest worry for the time being is how to make money in the age of on-demand streaming. Speaking of which.

Spotify snapped up music intelligence and data platformEcho Nest back in 2014, and off the back of that acquisition has been doubling down on its music recommendation efforts. The star of the show is Discover Weekly, a personalized playlist of music built around songs youve previously listened to on the platform.

In effect, Spotify analyzes your history and meshes it withthe listening behavior of othersto see what songs commonly appear next to each other, then based on this information itrecommends new music. Andit is more than pretty good it is pretty excellent. While Apple is banking on human curators via the likes of Apple Radio, Spotify is arguably winning the music-recommendation battle using algorithms and automation.

Whats most interesting about this is that it is infinitely more scalable than a humanDJs ability to recommend new music. Playlists built on algorithms are always tailored to the individual, while human recommendations will always have biased subjectivity weighted against it that will never appeal to everyone at all times.

Similarly, Shazam analyzes song structure to tell you what the name of the song is and who performs it.All you need to do is hold your phone up, tap a button, and voila. It really is a great way to discover new music and build up a library of tunes that you encounter on your day-to-day business, be it in a shop, at a football stadium, or while watching TV. Such technologies make everyone an expert, without having to become an expert. Youdont need to knowanything except how to tap a button to identifya song, while Shazam links in directly with Spotify and iTunes to make it easy to stream or buy music.

Together, the likes of Spotify and Shazam could put a sizable dent into the knowledge-powered smarts of music writers and DJs around the world. People have instant access to all the information they need on the music they hear around them. And why listen to the top 10 charts on the radio, or read the top 5 albums of the week inthe NME, when you know that Spotify has all the best new music? And why turn to your music-obsessed buddy to ask what the name of the song in that TV advertisement is when you can just Shazam it?

With algorithms at work, the need for human knowledge and expertise diminishes.

Above: Lego robot typing

Its difficult to envisage a time when a machine will be capable of crafting a best-selling novel, but lord knows geeks have been trying to make that happen for a while. Its not overly difficult to create something that is formed of words and roughly comprehensible in parts, but generating something with a proper narrative that flows beautifully from start to finish and is infused withwit and passion well, that could be a long way off yet.

But we are already at a stage where machines are producing journalistic content (for want of a better phrase). Last summer,the Associated Press (AP) revealed it was expanding its baseball coveragewith automated stories generated by algorithms through a partnership with Automated Insights.The AP had worked with Automated Insights for years already, generating thousands of computer-generated corporate earnings reports.

Automated Insights uses artificial intelligence to analyze big data and transform it into stories. Chicago-based Narrative Science offers something similar, with a specific focus on business intelligence for the enterprise, or data storytelling, as it puts it.

Heres an AP report from a baseball gamein the New York-Penn league, powered by Automated Insights.

STATE COLLEGE, Pa. (AP) Dylan Tice was hit by a pitch with the bases loaded with one out in the 11th inning, giving the State College Spikes a 9-8 victory over the Brooklyn Cyclones on Wednesday.

Danny Hudzina scored the game-winning run after he reached base on a sacrifice hit, advanced to second on a sacrifice bunt and then went to third on an out.

Gene Cone scored on a double play in the first inning to give the Cyclones a 1-0 lead. The Spikes came back to take a 5-1 lead in the first inning when they put up five runs, including a two-run home run by Tice.

Brooklyn regained the lead 8-7 after it scored four runs in the seventh inning on a grand slam by Brandon Brosher.

State College tied the game 8-8 in the seventh when Ryan McCarvel hit an RBI single, driving in Tommy Edman.

Reliever Bob Wheatley (1-0) picked up the win after he struck out two and walked one while allowing one hit over two scoreless innings. Alejandro Castro (1-1) allowed one run and got one out in the New York-Penn League game.

Vincent Jackson doubled twice and singled, driving in two runs in the win. State College took advantage of some erratic Brooklyn pitching, drawing a season-high nine walks in its victory.

Despite the loss, six players for Brooklyn picked up at least a pair of hits. Brosher homered and singled twice, driving home four runs and scoring a couple. The Cyclones also recorded a season-high 14 base hits.

This story was generated by Automated Insights (http://automatedinsights.com) using data from and in cooperation with MLB Advanced Media and Minor League Baseball, http://www.milb.com.

And heres an earnings report in Forbes, powered by Narrative Science.

Over the past three months, the consensus estimate has sagged from $1.25. For the fiscal year, analysts are expecting earnings of $5.75 per share. A year after being $1.37 billion, analysts expect revenue to fall 1% year-over-year to $1.35 billion for the quarter. For the year, revenue is expected to come in at $5.93 billion.

A year-over-year drop in revenue in the fourth quarter broke a three-quarter streak of revenue increases.

The company has been profitable for the last eight quarters, and for the last four, profit has risen year-over-year by an average of 16%. The biggest boost for the company came in the third quarter, when profit jumped by 32%.

Earnings estimates provided by Zacks.

Narrative Science, through its proprietary artificial intelligence platform, transforms data into stories and insights.

Such reports wont be winning any Pulitzer prizes yet, but theyre perfectly readable and the algorithms are constantly improving. Theres no evidence that machines will be capable of producingsomething akin to Dickens or Proust, but who knows what another 10 years worth of data could do to improve their writing smarts?

A machine will win a Pulitzer one day,noted Narrative Science chief scientist KrisHammond, in the Guardian. We can tell the stories hidden in data.

While fears abound that algorithms will kill off human journalists, figuratively speaking, the AP has previouslystated that embracing machine-written stories is more about expanding its coverage than replacing journalists. Through this method, it can cover many more Minor League Baseball games it would not have previously covered, simply by using data provided by news and statistics body Major League Baseball Advanced Media (MLBAM).

Augmented content was never intended to replace human-generated content, explained Joe Procopio, Automated Insights chief innovation office, in an interview with VentureBeat.Its another tool, another arrow in the journalists quiver, so to speak, and it should be used in places where it can take a lot of the data science and number crunching off the journalists plate. That frees up the journalists time to be able to do more of the investigative and reasoning work inherent in their jobs.

What will ultimately decide whether an artistic endeavor is replaced by an algorithm or set of algorithms, in a business setting at least, is whether its more efficient. The question is: Does it save time and money without compromising on quality?

There are basically two boxes that need to be checked when deciding to use automation to tell a story, added Procopio.One,is the data available to write something compelling, and two, is the business case there in other words, does automation save enough time and resources to make it worthwhile?

So can a machine be trained to amend its style of writing depending on whether its writing an earnings reports, a baseball review, or an obituary? Absolutely this is already happening. Could a machine write a review of a music gig? Or write up an interview? Potentially, but it all comes down to the quality of the data the platform is given,and whether its actually cost effective totrain a system to become efficient at such write-ups.

Automation can be used when writing the types of pieces you describe feature, interviews, reviews, etc., where automation makes sense, continuedProcopio. How much of the piece should be automated depends on the scope of the piece.

Whats emerging here is that such tools could be more about assisting the journalist than replacing them. It might not make sense to attempt entire computer-generated write-ups of a music gig, for example, if it already requires a human to attend the gig and form an opinion. But it maymake sense to use a machine to fill in the gaps in the final review, or even to format it properly. For example, automation could generate paragraphs on a particular bands sales and downloads, or maybe ticket sales, through tapping existing databases that contain up-to-date information. Its not really important whether a human or a machine finds and compiles such data, so long as its accurate, but using an automated approach could save a journalist a lot of time.

Away from the journalistic sphere, the global translation and interpretation industry is reported to be worth around $40 billion. And contrary to whatsome may think, the process of converting words and meanings between languages requires a great deal of creativity.Often words or sentiment dont convert well between languages and vernaculars, leaving the translator to trawl the nuanced depths of their linguistic abilities to communicate the intended meaning in another tongue.

Historically, machine translation tools have had a bad rap, but they are getting better. Its now possible to plug any foreign-language newspaper article into Google Translate and receive a pretty faithful interpretation in another language, thoughthere are many colloquialisms that will still trip up the best machine translation tools out there.Google has started using its AI-based neural machine translation across more of its public-facing services.

Skype also has a real-time voice translation tool, which lets you speak with someone (verbally) in a foreign tongue such as Japanese,in real time. Skype Translator uses AI smarts such as deep learning to train artificial neural networks, meaning it should improve over time as it listens to more conversations.

Any business worth its salt would not rely 100 percent on machine translations for mission-critical communications with customers. But we are certainly fast approaching a stage where machines can be called upon for less important stuff, and perhaps used in tandem with a proofreader to correct mistakes and clarify any ambiguities made by the machinefor use in more important communications.

So, as with Automated Insights, we could have a situation where 100 percent automation is used in some instances where it makes sense, but in cases where the nuanced understanding of a human is needed, the two would work in conjunction with each other.

Its clear that the threat from automation to human jobs is real for many industries, and that includes the creative realm: streaming services that serve you the perfect playlist, apps that turn a family photo into something straight from Van Goghs easel, real-time translations and interpretations, robot-written news reports, and websites created automatically simply by answering a few questions.

This leads us to one stark question. Creativity isacore defininghuman trait, something that truly separates us from the machines, sowhere is the incentive to get creative when all these tools out there are setting out to save us from doing it ourselves?

There are a number of positives here. If a computer was to get as good as, or better than, humans at drawing in a natural style, then it could become the teacher, or assist an artist in their own creative process. Plus, there is a strong line of argument that says that people will always have a creative streak and will want to do things themselves. If you can click a button to turn a photo into a work of art, where is the fun in that?

And that is something that humans will never lose: a desire to have fun and make things themselves. Whether they will be able to get a job off the back of it in 20 years time is another question, of course.

When technology is constantly fixing human errors, be it a typo in a Word document or a wonky line in a drawing, humans may gradually lose the ability to perform certain creative taskswithout computer intervention.Its no longer necessary to remember facts, or phone numbers, or routes to your grandmas house in the next town, because we know its all instantly accessible through a phone. This surely has an impact on a brains ability to remember things. Similarly, if kids grow up with tools to help them draw on their phone or computer because itsslow and difficult otherwise, this cant bode well if it becomes the norm.

But lets not get too carried away. Machines have yet to prove theyre up to the job of many creative tasks; all theyve shown so far is they can chip away at the edges and even then they still need human assistance. Highly creative projects such as writing novels, writing investigative journalism, or penning an entire album of original music with heartfelt, meaningful lyrics its difficult to see a time in the near future where computers will trump humans.

A good example is this cool little short sci-fi film produced last year, called Sunspring. It stars real actors, but the script was written by a machine. It was inspired by Alphabets AlphaGo AI system beating a pro player at the age-old strategy game Go.

The script for the short film was authored by a recurrent neural network called long short-term memory, or LSTM for short, according to a report in Ars last year.It is actually really funny, and makes little sense, but it serves as a reminder as to how far behind machines are in terms of creating genuine works of art that humans would wish to enjoy at scale.

Its also important to distinguish between artificial intelligence and algorithmic intelligence. The former is more about computers being able to think, understand, and adaptin way a human might, while the latter is more about usingmathematics to help people and machines work together.

Phil Tee is chairman and CEO of Moogsoft, a company that specializes in bringing algorithmic intelligence to enterprises Moogsoft basically helps them adoptalgorithms to address mundane operational tasks. He told VentureBeat:

Artificial intelligence is the ability for computer systems to perform tasks that traditionally have required human intelligence, such as visual perception, speech recognition, decision-making and language translation. Algorithmic technologies such as Algorithmic IT Operations (AIOps), on the other hand, leverage mathematics to help operators navigate dynamic, and highly unpredictable settings such as enterprise IT environments. There isnt anything artificial about algorithms.

And this is a key point. Using algorithms to predict what music youll like on Spotify or what movies you should watch next on Netflix is smart for sure, but its not creative in itself. It may be better at doing its job than a human is, but it doesnt exist as part of the arts. So while well see businesses increasingly turn to algorithmic intelligence to optimize and streamline their operations and differentiate themselves from the competition, art itself may not be directly under threat.

But will we ever reach a stage where a computer could write a completely coherent book, song, or movie of its own volition?

Absolutely, but the advances necessary are quite imposing, added Tee.The typical neural network today has roughly hundreds to tens of thousands of neurons, which makes it even less intelligent than a sea slug, which has 18,000 neurons in its brain. This journey to a creative thinking machine is vital, but a long one. Perhaps we should be more focused on intelligence as an aid to creativity rather than a replacement. After all, creativity probably is ultimately what defines humanity.

Art needs humans, and humans need art. Machines may increasingly help the two work together, and it may even replace some jobs, but as one of our defining characteristics, humans and art will continue to be inseparable.

See the rest here:

The art of algorithms: How automation is affecting creativity - VentureBeat

There are IT jobs that you just know are built for failure. They are so big and cumbersome and in some cases are plowing through new ground that unforeseen outcomes are likely. Then there are other situations where an IT pro might just say whoops when that unforeseen result should have been, well, foreseen.

UpGuardhas pulled together a group of the biggest instances in the past few years in which the well-intentioned automation of a companys IT systems facilitated a major breach instead.

Healthcare.gov: How an oversight broke the U.S. governments healthcare website

When the U.S. government rolled out the Affordable Care Acts web enrollment tool, Healthcare.gov, in October 2013, it was expected to be a monumental undertaking; and with the delivery of millions of citizens health insurance on the line, the stakes were high. So, when a major software failure crashed the website a mere two hours following its launch, the White House administration suffered a sizeable backlash. Due to a lack of integration, visibility, and testing, the project had significant problems from the start beginning with over 100 defects with Healthcare.govs account creation feature, dubbed Account Lite.

Given its function, Account Lite was a crucial piece of the Healthcare.gov site, serving as the mechanism by which people would create their accounts and gain access to their healthcare options. This particular module had so many problems that it was assuredly a disaster waiting to happen. Nevertheless, contractors moved forward with it as it stood.

The software release failed, preventing millions from securing healthcare coverage. Whats more, the outage had political ramifications as critics of the Affordable Care Act began citing the outage as evidence of the administrations inability to develop a successful healthcare program. The site was eventually stabilized, but the work that should have been integrated before the release was completed only after the crash occurred.

Dropbox: The buggy outage that dropped Dropbox from the web

No IT team enjoys the experience of an outage, especially when it kicks off a race for your team to implement its emergency procedures. In January 2014, Dropbox found themselves scrambling in this very scenario, when a planned product upgrade took down the sites for three hours.

When a subtle bug in the Dropbox script automatically applied its updates to a small number of active machines, it affected Dropboxs thousands of production servers and caused the companys live services to fail. Fortunately for Dropbox, its emergency procedures were well designed and largely effective.With its backup and recovery strategy, the IT team was able to restore most of their services within three hours. For some of the larger databases, however, recovery was slower taking the company several days for all of its core services to fully return.

Amazon/DynamoDB: When the DynamoDB database disrupted all of Amazons infrastructure

Just as physical services like freight haulage require physical infrastructures like roads and highways, companies digital services depend on underlying digital infrastructures. When some of Amazons automated infrastructure processes timed out in September 2015, their Amazon Web Services cloud platform suffered an outage. Cascading from a simple network disruption into broad service failure, Amazon experienced a network outage like those traditional on-premise data centers experience, despite its very advanced and integrated cloud platform.

Amazon had a network disruption that impacted a portion of its DynamoDB cloud databases storage servers. When this happened, a number of storage servers simultaneously requested their membership data, exceeded their allowed retrieval and transmission time. As a result, the servers were unable to obtain their membership data, and subsequently removed themselves from taking requests.

When the servers that became unavailable for requests began retrying the requests, the DynamoDB timeout issue manifested itself in a broader network outage. Just like that, a network disruption started a vicious cycle and affecting Amazons customers as it took down AWS for 5 hours.

Opsmatic: recipe for disaster

When managed under traditional server administration, automation often faces the same set of age old IT problems. One of those classic, faulty assumptions is if it aint broke, dont fix it assuming that all systems are operating the way they should be. When Opsmatics routine server maintenance shut down its whole operation, it was because things werent exactly as they had thought.

In Opsmatics case, a Chef recipe called remove_default_users had been created during the early stages of the companys Amazon Web Services experimentation. Now, long after the test, that recipe was somehow still running against the production servers, unbeknownst to the staff maintaining them.

Like many major outages, this incident was the result of a long, causal sequence of mistakes, none of which were caught until they added up to a giant problem.

Knight Capital: How one tiny mistype cost Knight Capital $1 billion

Knight Capital automated not only its administrative IT processes, but also its algorithmic trading. Unfortunately, this meant that changes and unplanned errors in handling real money could happen very quickly. This is the story of how a single error caused Knight Capital to lose $172,222 per second for 45 minutes straight in 2012.

When operating a data center at scale, clusters of servers often run a single function. This distributes the load across more computing resources and provides better performance for high traffic applications. This model requires all the servers in a cluster to use the same configurations, no matter which particular server in the cluster they are using, so that all the applications will behave the same way. However, configurations even if identical at provisioning always drift apart.

Despite all of its automation, Knight Capital was still manually deploying code across server banks, and an inevitable human error caused one of its eight servers to have a different configuration from all the others. When one of Knights technicians made this mistake during the deployment of the new server code, no one knew. Thus, from that point forward, the IT staff were operating under the misconception that these servers were identical.

At the same time, a decommissioned code remained available on the misconfigured server. As a result, this server began sending orders to certain trading centers for execution, and the error triggered a domino effect around algorithmic stock trading costing Knight Capital $465 million in trading loss.

Delta Airlines: automated fleet of flightless birds

Large logistics operations rely on automated systems to achieve the necessary speed to perform at scale. Some airlines struggle to keep those systems functional. Just like traditional, manual methods of systems administration, automated systems suffer from misconfigurations. In the worst-case scenarios from recent years, failure of these systems has cost airlines hundreds of millions of dollars and more in their customers goodwill.

When misconfigurations occur, they are pushed out quickly through automated mechanisms and can bring entire systems down. For airlines, this means flight operations are interrupted, planes are delayed, and money is siphoned out of the business. In one such case in January 2017, Delta told investors that one glitch in their automated system caused an expansive outage, costing the airline more than $150 million.

Google Gmail: Youve got mail?: Gmails 2014 bug-induced failure

When technology giants experience the occasional automation-related outage, an hour of downtime can mean a lot more. For these huge organizations to make any sort of change, they have to do so across thousands of servers. Having always been on the bleeding edge of technology, its no surprise that Google has automated its configuration management. Although employed to make operations easier, when the wrong change is executed in an automated system that means it can propagate far and wide within a matter of seconds.

In 2014, a bug in Googles internal automated configuration system caused Gmail to crash for around half an hour. The incorrect configuration was sent to live services, causing users requests for their data to be ignored, for those services, in turn, to generate errors.

The lesson is that configuration automation is not the same as configuration management. Automation ensure that changes get pushed out across all systems.

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The 7 worst automation failures - CSO Online

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To accelerate the development of autonomous cars, General Motors will spend $14 million on a new facility in San Francisco for Cruise Automation.

GM bought the driverless tech startup last March, and is making a major push to expand its resources. The new R&D facility, which will be ready by the end of the year, will more than double Cruise Automations current research and development space. California has provided GM with an $8 million tax credit to help with the expansion. Over the course of five years, Cruise Automation will add more than 1,100 new employees.

Founded in 2013, Cruise Automation originally sold aftermarket self-driving systems on its own, but it now operates as an independent company within General Motors. Currently, its testing more than 50 autonomous Chevrolet Bolts on public roads in San Francisco, Arizona, and Michigan. Although its unclear when the self-driving tech will make its way to market, we can expect it to arrive first on vehicles for Lyft. Last year, GM announceda partnership with Lyft to create a network of on-demand autonomous vehicles across the U.S.

Expanding our team at Cruise Automation and linking them with our global engineering talent is another important step in our work to redefine the future of personal mobility, said GM CEO Mary Barra in a press release. Running our autonomous vehicle program as a start-up is giving us the speed we need to continue to stay at the forefront of development of these technologies and the market applications.

GM recently came in second place in a study ranking the top players in autonomous driving technology. It jumped ahead of Nissan, Daimler, Volkswagen, BMW, Tesla, and a host of other companies, but slotted just behind Ford.

Source: GM

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GM Expands Cruise Automation With $14 Million Investment - Motor Trend

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There are two things we know for sure about how automation impacts jobs. First, we know that some of the work being done by humans today will be done by machines tomorrow. The robots are, in fact, coming for our jobs. However, another thing we know for sure is that this has been happening since the industrial revolution and the economy has always adjusted to accommodate the changes. The job market continues to expand even though automation changes the landscape. Will this continue to be the case going forward? Or, could things be different this time around thanks to advancements in automation that allow machines to replace more than just human muscle?

Its good to know what to expect, no matter what the future holds. One of latest studies on how automation could impact the job market, worldwide, comes from the consulting firm PricewaterhouseCoopers. This study revisits data from two other studies (one by Oxford University and the other from OECD analysts).

According to this analysis, 38 percent of U.S. jobs are at risk of being lost to automation by the early 2030s, compared to 35 percent of jobs in Germany, 30 percent of jobs in the U.K., and 21 percent of jobs in Japan.

Analysts explained that the differences exist because of variance within specific industries. For example, within the financial and insurance sector, 61 percent of U.S. jobs were deemed high-risk, while only 32 percent of jobs were labeled as such for the U.K. It all comes down to education and skill level, which has always been a huge factor in how much jobs are threatened by automation.

The jobs of these US retail financial workers are assessed by our methodology as being significantly more routine and so more automatable then the average finance sector job in the UK, the report stated, with its greater weight on international finance and investment banking.

Theres nothing new about trying to understand how automation might impact jobs and the economy or even fearing the impact. In the introduction to this report, the authors reviewed the history of the Luddites, who protested automation back in the early 19th century. In hindsight, the Luddites were wrong. While newer technologies did change the job market, they also created far more jobs than they eliminated. In the U.K., people who still worry that technology will negatively impact the economy are often dismissed and labeled as believers in the Luddite fallacy.

However, some argue that this fallacy might not hold true for much longer. Thanks to improvement in technology, more and more jobs are being replaced that require more than a physical investment. Robots can now do jobs that used to demand a humans brainpower.

The authors of this report also acknowledge that the technology thats emerging today is a bit different than what weve seen before. Todays smart technology is able to replace more than just human bodies. Now, machines can stand in for humans on an intellectual level, too.

Will this just have the same effects as past technological leaps short term disruption more than offset by long term economic gains or is this something more fundamental in terms of taking humans out of the loop not just in manufacturing and routine service sector jobs, but more broadly across the economy? researchers asked in this report. What exactly will humans have to offer employers if smart machines can perform all or most of their essential tasks better in the future?

The answer to these questions, to some extent, is unknown. Well have to wait and see how these technological advancements play out over the course of years across our economy. However, as long as certain constraints are in place, the authors of this report seem optimistic that the job market will adjust and grow stronger as a result of this progress, as it has done in the past. Here are a couple provisions they highlighted.

Also, here in the U.S., labor economists say that the transition can be eased through things like stronger unions, the creation of more public-sector jobs, more college degrees, and a higher minimum wage. These factors are deemed much more impactful than some of the other issues that get attention.

Over the long haul, clearly automations been much more important its not even close, Lawrence Katz, an economics professor at Harvard told The New York Times when discussing how automation impacts our job market compared to threats from offshoring and immigration.

He went on to add, Just allowing the private market to automate without any support is a recipe for blaming immigrants and trade and other things, even when its the long impact of technology.

There are still a lot of unanswered questions when it comes to understanding the future of automation and what it means for tomorrows workers and the job market. However, it will help to be prepared for whats to come. Workers, companies, and state and federal governments should be proactive and work toward adjusting around these changes. Thats the best way to ensure that technology continues to play a positive role in our economy, as its done in the past, rather than the reverse.

How do you think we ought to prepare for a future with smart automation? We want to hear from you! Leave a comment or join the discussion on Twitter.

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Smart Automation Poses New Challenges to the Job Market - PayScale Career News (blog)