What do companies like Netflix, Airbnb, Spotify and Lyft know that you don’t know? What do these companies have that you might be lacking?

Sure, they had an initial idea. But we’ve all had good ideas. How did each of these new market heroes move from the seed of an idea to the disruption of an entire industry?

For each of them, it took a combination of strong technological and analytical skills to develop a digital platform that turned data into a fundamental asset for the company.

Now you might be thinking: But we do analytics, too. We have a smart team of data scientists. We know the latest techniques for machine learning and deep learning, and we even attend the best analytics conferences.

But there’s a difference between analytical maturity and technological maturity. Disrupters are both analytically and technologically mature.

They don’t just understand how to build models and use algorithms. They also know how to design systems that incorporate algorithmic thinking into their core. They know how to replicate and tweak models by the thousands, how to deploy models at scale and how to test, iterate, improve and retire models systematically.

Compare your analytic maturity to peers in your industry, and determine where you have room to grow. Many traditional companies are analytically mature but not technologically mature.

If you’ve done a great job of capturing and analysing data but come up short on moving those results into action on a large scale, you’re in luck.

We’re seeing three clear shifts in the industry that are making it easier for even the most traditional companies to work like disrupters and change the way they deploy analytics. They are:

1. Intelligent data preparation.
2. Containers for analytics.
3. ModelOps for machine learning.

 The convergence of these three technologies has the potential to change the way you work with data and analytics – so you, too, can think like a disrupter.

Intelligent data preparation

Data has always been a problem. Data scientists still spend a lot of time manipulating data. And now that you have more – and more complex – data, how will you deal with it? What if we recast data management as a problem with an AI solution?  

Intelligent data preparation uses AI algorithms to recognise patterns in data, understand what data belongs together and provide context around data.

Right now, training a data set is usually a manual process that requires a lot of labelling. You take a piece of data or an image and add metadata. You start by saying, this is a cat, this is a dog, this is a barn. You label all that data and then feed it to a training set. In a three-dimensional world of video, you might label plywood, a corner of plywood, the length of the plywood.

Instead of requiring humans to tag and label the data, intelligent data preparation offers auto tagging and auto labelling. It uses reinforcement learning to create reason-based algorithms. It can even teach models to learn from other models and then to retrain the model itself. It’s continuously learning based on the decision it’s making.

Intelligent data preparation isn’t limited to image data. You can also use it across the board on streaming data, static data, master customer data – and more. It can join columns, clean fields and even combine warehoused data with streaming data – all automatically.

Disrupters have these capabilities built into their systems already. They are working with traffic data, hospitality data, streaming data – and more – and processing it intelligently for thousands of business decisions every day.  

Containers for analytics

In the world of IT, containers are the latest essential for deploying software in cloud environments. They fit nicely into the way IT likes to test and run software because they deploy software faster, manage upgrades simply and make it easy to combine different software packages. You can try things out without forcing a hard upgrade.

For software providers, it also makes it easier to build packaged deployments, integrate with other packages and add more automation into a system to self-optimise for customer workloads or other requirements.

Ultimately, containers will help to democratise the use of advanced analytics and lower the barrier to entry for trying new software products. They make it very easy for you to get software at the pace you want to consume it. And you can experiment with new algorithms or new techniques without a lot of upfront risk or expenses.

Imagine, for example, that you’ve deployed a machine learning algorithm for a next-best offer. When a new algorithm becomes available for that same task, you can automatically download that and funnel five per cent of your traffic through this algorithm. Then you let the machine tell you if it’s performing better than the previous algorithm.

If it is, the machine starts funnelling 10 per cent of visitors through the new algorithm, then 25 per cent and so on until you’ve replaced the algorithm altogether. If, on the other hand, the results are not improved, it returns the algorithm and goes back to how it was doing it before.

The disrupters are constantly testing new algorithms and running new programs in this manner. It’s one way they keep improving with small increments at scale. With containers, you’ll be able to do the same.

ModelOps for machine learning

How do you cycle machine learning models from the data science team to the IT production team? Do you do regular deployments and updates? Do you watch for models to degrade and take action? Do you put all of your best models into production?

ModelOps is a process you can use to move models from the lab to validation, testing and production as quickly as possible while ensuring quality results. It helps you manage and scale models to meet demand and continuously monitor them to spot and fix early signs of degradation.

If you use this ModelOps process, you will put more models into production and see continued results:

• Data: Explore and access data from a trusted and secure source.
• Develop new models: Create models with deployment and monitoring in mind.
• Register models: Preserve data lineage and track-back information.
• Deploy models: Improve deployment speeds with close collaboration between data scientists and IT.
• Monitor models: Consistently track performance, and then retrain or replace models as needed.  

 This process is designed to be sensitive to data fluctuations, model bias and model degradation. It improves time to deployment for new models and ensures regular updates.

A solution for ModelOps can help you compete with the disrupters who have perfected this process while deploying and monitoring models by the thousands.

Work like a disrupter

The three technology developments discussed above are changing the way we manage data and roll out analytics projects. If you’re already analytically mature, you can use these new technologies to become technologically mature as well. Use these tips as first steps on your journey:

1. Pick an area where you have ongoing data management issues and try to tackle them with intelligent data preparation.
2. Look at where you have complex software and code footprints and see if containers can help simplify your infrastructure for analytics.
3. Examine your existing data science work and see where you can benefit from a governed process for managing, deploying and updating models at scale.

If, on the other hand, you are technologically mature with a strong IT infrastructure, but not analytically mature, you can benefit from these technologies too:

1. Consider what outside data sources you can bring in if you used intelligent data preparation, or what advanced algorithms you might be able to develop if your data was more intelligent.
2. Look into using containers for analytics, not just for your operational systems. The same best practices you’ve learned for automation and portability in those systems will help your analytics efforts too.
3. Determine where you could make the biggest impact with machine learning models that are managed as a corporate asset and start to experiment with advanced analytics while keeping your software development strengths intact.

No matter where you are on the continuum from traditional to disrupter, you can benefit from exploring the technologies described here.

It can be a challenge to advance on the scale of technological maturity, but I’ve seen it happen. I’ve watched markets shift and opportunities open up as traditional companies took their analytical skills and paired them with new technological skills. It could be you next.

The post To work like a disrupter, change the way you work with data and analytics appeared first on Artificial Intelligence.

Huawei has unveiled its new smart cities digital platform utilising artificial intelligence (AI) and Internet of Things (IoT) capabilities, which it said could be used across smart public safety, environmental protection, transportation, government, education, and agriculture.

Huawei’s +AI Digital Platform connects what it calls the brain or command centre; the central nervous system, or network; and the peripheral nervous system, made up of sensors across a city.

“Just like an operating system, the platform is compatible with different city sensors, creates a city digital twin, and supports diverse city applications,” Huawei Enterprise Business Group VP Ma Yue said.

The smart cities digital platform combines AI, IoT, big data, a geographic information system, video, cloud, converged communications, and security.

“Huawei has also developed a middleware platform to provide services to software application partners. This is designed to help application partners quickly develop upper-layer applications to accelerate transformation and innovation in city management, city services, and industry development,” the Chinese networking giant added.

According to Huawei, smart cities solutions are now in their fourth stage thanks to the addition of greater AI capabilities.

“A smart city development race driven by the growing global digital economy is taking place around the world,” Huawei said.

“Smart city adoption has undergone the first stage of breaking down data silos; the second stage of the rise of mobile internet applications; and the third stage of IoT deployment for a collection of mass volumes of city data.

“It is now at the fourth stage, where cities are improving their management capabilities through AI-enabled data mining, achieving the integration of digital technologies and city governance to promote sustainable city development.”

Huawei said its smart cities solution has now been deployed across more than 160 cities in 40 nations, including in Duisburg, Germany; Rustenburg, South Africa; and Tianjin’s Binhai New Area.

Huawei last month unveiled its AI strategy and portfolio, including a series of chips, cloud services, and products.

The company’s Ascend AI chip series includes the Ascend 910 and Ascend 310, with the company also unveiling the Compute Architecture for Neural Networks (CANN), a chip operators library and automated operators development toolkit, and MindSpore, a device, edge, and cloud training and inference framework.

The latter includes “full-pipeline services, hierarchical APIs, and pre-integrated solutions”, Huawei said, with the Chinese networking giant to later expand its AI stack to include an AI acceleration card, AI server, AI appliance, and other AI products.

“Huawei’s AI strategy is to invest in basic research and talent development, build a full-stack, all-scenario AI portfolio, and foster an open global ecosystem,” rotating chair Eric Xu said in October.

Huawei at the same time announced its smart cities AI partnership with Tianjin Binhai New Area, as well as a smart campus solution and joint innovation laboratories alongside Chinese real estate developer Vanke.

“The Tianjin Economic-Technological Development Area designed and developed an AI-based ‘1 + 4 + N’ smart city solution, which refers to one centre, four platforms, and additional innovative applications,” Huawei said.

The centre is Huawei’s “city brain” Intelligent Operations Centre, which aggregates and processes data collected from the government, businesses, and citizens through IoT applications and internet access.

The four AI platforms are then Resident Voices, which has voice recognition for all citizens of Tianjin; Sensing the City, which uses image recognition across people, places, vehicles, and things “for the purpose of fostering harmony for all”; Resident Care, which involves deep learning and correlation for personalised services for citizens; and Enterprise Services, which ensures services availability match their need by applying “multi-dimensional and correlation analysis to clarify the internal relationships of industries in the TEDA district”.

Huawei’s AI push saw it sign a strategic agreement with Chinese search engine giant Baidu in December last year to build an open mobile AI ecosystem that covers platforms, technology, internet services, and content ecosystems.

Huawei head of Consumer Software Engineering and director of Intelligence Engineering Felix Zhang had last year said the addition of AI capabilities to smartphones will bring the next shift in technology.

Huawei had unveiled its Kirin 970 chipset with built-in AI in September 2017, at the time calling it the “future of smartphones”. Its mobile AI is made up of a combination of on-device AI and cloud AI.

Huawei had in May also announced the launch of its eLTE Multimedia Critical Communications System (eLTE MCCS), which it said provides “ultra-reliable” communications solutions for public safety organisations.

According to Huawei, the narrowband networks traditionally used for public safety are limited to providing access to basic voice services. The eLTE MCCS service uses a mobile service convergence platform to interconnect such networks with video surveillance and GIS.

The post Huawei unveils artificial intelligence smart cities platform appeared first on Artificial Intelligence.

Corporations have been relying on Learning Management Systems (LMS) to faciliate learning and address the challenges of distance, time and cost factors. LMS implemented in most organisations have been facilitating in hosting content, providing content access to the learners, conducting assessments and keeping track of their progress. The expectation of training managers in the ’90s was for technology to deliver static e-learning built around the digital libraries. LMS transitioned to becoming a platform for talent management in the next decade. Today the requirement is for continuous learnin. It is expected to be data driven and mobile friendly. With the arrival of Industry 4.0 and digital functioning, traditional LMS is no longer equipped to cope with the transformational business landscape and the emerging needs of the employees as an aid to their performance. In the new era, analytics will lead and drive every function in the organisation. Learning is also expected to be influenced by data, to become machine driven, more intelligent and develop the ability to anticipate learning needs.

Businesses are therefore evaluating the question of whether to retain the LMS and upgrade them or replace them with digital learning platforms. The survey findings of most organisations have been highlighting that the static content with the boundaries of learning pre fixed for all individuals which is typically the case with most LMS. Therefore need of the hour is ‘just in time content’ rather than just ‘in case content’ and alignment of learning outcomes with the business outcomes. Digital learning platforms differ from the traditional LMS fundamentally on account of their design around key principles namely, the importance of personalisation, the recognition of the individual’s identity in the context of highly networked environment and the necessity to support this persona and the significance of the overall learner experience which is the key to retaining the attention and the interest of the learners. Just as in the consumer buying process where personalisation and customisation are essential for successful business models, it is important to recognise that the learner experience is critical to sustaining interest in the learning process. For this, the learner profiles and their learning requirements have to be understood—the needs could be preparatory for a role, some would be just in time and some others could be remedial.

Apart from organisation-designed content, digital learning platforms such as Skills Alpha provide access to numerous external channels of learning and also nurture networked learning and user generated content. Courseera, YouTube, Udemy, to name a few, are frequented by all and by integrating these channels into the platform, it becomes feasible to appeal to varying learning patterns and styles of the employees and thus add a significant depth and dynamic edge to the content. Further, byte sized and video based content are preferred increasingly as learners have short span of time and this trend has to be addressed by the digital platform. Coaching and feedback from on the job training are other useful dimensions of learning thus providing a seamless experience for the employees. As a result, training managers need to focus on content curation instead of original content creation. Often we come across an incremental approach to ‘digital learning’. In other words, some managers would consider digital learning as a proposition that takes place with the access to intranet, social media, LMS and assessments and employees are able to move from one medium to another for their learning needs on their own, supplemented by offline initiatives such as performance management or competency matrix. This approach fails to deliver the optimal learning experience as seamless navigation and data capture at every stage, personalisation and collaboration with custom views for different stakeholders are not feasible with this approach.

One significant factor which is impacting most performers in organisations is the urgent necessity to embed analytics driven approaches to their functioning as the roles are becoming more complex. Digital learning platforms provide analytics support to business managers and L&D managers. Deep analytics could help teams develop personalised plans and monitor their progress. Investments in technology can be justified only if the correlation can be established between learning outcomes and business outcomes such as sales growth, employee engagement, customer satisfaction index, attrition rates and employee engagement score. Just as customer engagement parameter has become morescientific and data driven, it is also expected that the measurement of learning effectiveness becomes more accurate and reliable. Digital learning platforms with their built-in analytics and the capability to bring together multitudes of relevant content in a dynamic fashion could be the game changer for businesses working on the transformation agenda.

The post Digital learning needs help from deep analytics appeared first on Artificial Intelligence.