We have now entered an era with a new virtual revolution, particularly, the Internet of things (IoT). The virtual revolution marks the starting of information age. We use the Internet almost every day. The net has turned out to be one of established ways for us to work together, to share our lives with others, to shop, to teach, to research, and to learn. However the next wave of the Internet isn't about people. it's far about things, honestly?
All about IoT
IoT is defined as the network of physical objects that can be accessed through the Internet. These objects contain embedded various technology to interact with internal states or the external environment.
IoT is characterized as "the figuring frameworks of sensors and actuators associated by systems, where the processing frameworks can screen or deal with the status and actions of connected objects and machines, and the connected sensors can likewise screen the characteristic world, individuals, and creatures." The center of IoT is not just about interfacing things to the Internet. It is about how to generate and use the big data from the things to make new values for individuals, and about how we empower new trades of significant worth between them. In other words, when objects can sense and communicate, IoT has its knowledge to change how and where choices are made, and who makes them, and to pick up a superior esteem, solution or service.
Fundamental to the estimation of IoT is in actuality the Internet of smart things (smart IoT). Supported by intelligent optimization, smart IoT can increase productivity of work and enhance quality of lives for people. Let us take “cities” — the engines of global economic growth — as an example. Smart cities have the potential to dramatically improve the lives of everyone. In intelligent transportation systems (ITS), smart IoT can not only monitor the status of the transportation, but also optimize traffic signal controls to solve traffic congestion and provide the travelers with better routes and appropriate transportation information, etc. Combining IoT and machine learning (ML) can also make our roads safer. Profits by smart IoT have been shown also in health-care, logistics, environment, smart home, in the aspects of better quality, energy conservation, efficiency increase, and so on.
Smart IoT remains in its infancy now in terms of the technology development and the effect on our global economy system and our daily lives. Maximum IoT statistics aren't used presently within the era of big data. Maximum IoT has no intelligence inside the generation of artificial intelligence (AI). IoT which might be used these days are on the whole for anomaly detection and control, as opposed to optimization and prediction. Given the brilliant anticipated increase of the Internet over the following 10 years, it is considered one of vital challenges and possibilities for us to invent and practice in real-global programs on a way to make the IoT smarter to generate the greatest value.
Machine Learning (ML) has revolutionized the world of computers by allowing them to learn as they progress forward with large datasets, thus mitigating many previous programming pitfalls and impasses. Machine Learning builds algorithms, which when exposed to high volumes of data, can self-teach and evolve. When this unique technology powers Artificial Intelligence (AI) applications, the combination can be powerful. We can soon expect to see smart robots around us doing all our jobs – much quicker, much more accurately, and even improving themselves at every step. Will this world need intelligent humans anymore or shall we soon be outclassed by self-thinking robots? What are the most visible 2017 Machine Learning trends?
2017 Machine Learning Trends in Research
In the research areas, Machine Learning is steadily moving away from abstractions and engaging more in business problem solving with support from AI and Deep Learning. In What Is the Future of Machine Learning , Forbes predicts the theoretical research in ML will gradually pave the way for business problem-solving. With Big Data making its way back to mainstream business activities, now smart (ML) algorithms can simply use massive loads of both static and dynamic data to continuously learn and improve for enhanced performance.
2017 ML Application Development Trends
Gartner’s Top 10 Technology Trends for 2017 predicts that the combined AI and advanced ML practice that ignited about four years ago and since continued unscathed, will dominate Artificial Intelligence application development in 2017. This lethal combination will deliver more systems that “understand, learn, predict, adapt and potentially operate autonomously. “Cheap hardware, cheap memory, cheap storage technologies, more processing power, superior algorithms, and massive data streams will all contribute to the success of ML-powered AI applications. There will be a steady rise in Ml-powered AI application in industry sectors like preventive healthcare, banking, finance, and media. For businesses that mean more automated functions and fewer human checkpoints. 2017 Predictions from Forrester suggests that the Artificial Intelligence and Machine Learning Cloud will increasingly feed on IoT data as sensors and smart apps take over every facet of our daily lives.
Democratization of Machine Learning in the Cloud
The democratization of AI and ML through Cloud technologies, open standards, and algorithm economy will continue. The growing trend of deploying prebuilt ML algorithms to enable Self-Service Business Intelligence and Analytics is a positive step towards democratization of ML. In Google Says Machine Learning is the Future, the author champions the democratization of ML through idea sharing. A case in point is Google’s Tensor Flow, which has championed the need for open standards in Machine Learning. This article claims that almost anyone with a laptop and an Internet connection can dare to be a Machine Learning expert today provided they have the right mindset.
The provisioning of Cloud-based IT services was already a good step to make advanced Data Science a mainstream activity, and now with Cloud and packaged algorithms, mid-sized ad smaller businesses will have access to Self-Service BI and Analytics, which was only a dream till now. Also, the mainstream business users will gradually take an active role in data-centric business systems. Machine Learning Trends – Future AI claims that more enterprises in 2017 will capitalize on the Machine Learning Cloud and do their part to lobby for democratized data technologies.
Platform Wars will Peak in 2017
The platform war between IBM, Microsoft, Google, and Facebook to be the leader in ML developments will peak in 2017. Where Machine Learning Is Headed predicts that 2017 will experience a tremendous growth of smart apps, digital assistants and mainstream use of Artificial Intelligence. Although many ML-enabled AI systems have turned into success stories, the self-driving cars may die a premature death.
Humans will Make Peace with Machines
Since 2012 the global business community has witnessed a meteoric rise and widespread proliferation of data technologies. Finally, humans will realize that it is time to stop fearing the machines and begin working with them. The InfoWorld article titled Application Development, Docker, Machine Learning Are Top Tech Trends for 2017 asserts humans and machines will work with each other, not against each other. In this context, readers should review the DATAVERSITY® article The Future of Machine Learning: Trends, Observations, and Forecasts, where the readers are reminded that as businesses develop a strong dependence on pre-built ML algorithms for Advanced Analytics, the need for Data Scientists or large IT departments may diminish.
Demand-Supply Gaps in Data Science and Machine Learning will Rise
The business world is steadily heading toward the prophetic 2018, when according to McKinsey the first void in data technology expertise will be felt in the US and then gradually in the rest of the world. The demand-supply gap in Data Science and Machine Learning skills will continue to rise till academic programs and industry workshops begin to produce a ready workforce. In response to this sharp rise in the demand-supply gap, more enterprises and academic institutions will collaborate to train future Data Scientists and ML experts. This kind of training will compete with the traditional Data Science classroom and will focus more on practical skills rather than on theoretical knowledge.
The Algorithm Economy will take Centre Stage
Over the next year or two, businesses will be using canned algorithms for all data-centric activities like BI, Predictive Analytics, and CRM. The algorithm economy, which Forbes mentions, will usher in a marketplace where all data companies will compete for space. In 2017, global businesses will engage in Self-Service BI, and experience the growth of algorithmic business solutions, and ML in the Cloud. So far as algorithm-driven business decision making is concerned, 2017 may actually see two distinct types of algorithm economies. On one hand, average businesses will utilize canned algorithmic models for their operational and customer-facing functions. On the other hand, proprietary ML algorithms will become a market differentiator among large, competing enterprises.
Some Thoughts to Ponder
If the threat of intelligent machines taking over Data Scientists is really as real as it is made out to be, then 2017 is probably the year when the global Data Science community should take a new look at the capabilities of so-called “smart machines.” The repeated failure of autonomous cars has made one point clear – that even learning machines cannot surpass the natural thinking faculties bestowed by nature on human beings. If autonomous or self-guided machines have to be useful to human society, then the current Artificial Intelligence and Machine Learning research should focus on acknowledging the limits of machine power and assign tasks that are suitable for the machines and include more human interventions at necessary checkpoints to avert disasters. Repetitive, routine tasks can be well handled by machines, but any out-of-the-ordinary situations will still require human intervention.
To know more about High-End professional training on ML, AI, IoT, Big Data, Cloud, Analytics, Data Science and more, feel free to drop a line at: [email protected]
This article originally appeared here.
In the recent Cricket world cup, every team had its own team of Data Analysts. They used various technologies like Cloud Platform and visualizations to predict scores, player performance, player profiles and more. Around 40 years’ worth of Cricket World Cup data is being mined to produce insights that enhances the viewer's experience.
- What would be the most profitable food served at the concession stand?
- What would be the best prices to sell game day tickets?
- Determine which player on the team is the most productive?
- Which players in the draft will become all-stars, and which ones will be considered role players?
- Understand the fans behavior at the stadium via their app and push relevant information accordingly.
The potential of big data just keeps growing. For taking full advantage, companies need to incorporate analytics into their strategic objectives.
A research report from McKinsey Global Institute (MGI), suggests that the opportunity and applications continue to expand in the data-driven world.
With rapid technological transformation, the question for businesses arises on how to position themselves uniquely in the world leveraging analytics. Over 2.5 quintillion bytes of data is generated every day. As information pours in via various digital platforms, VR application, and mobile phones the need for data storage capacity has increased.
The transformational potential
The recent progress shows the potential of big data and analytics in more than five distinct domains. However, transforming to a data-driven decision-making organisation is not always simple.
The first challenge is to incorporate data and analytics along with business objectives into a core strategic vision. Secondly, the lack of talent in the adoption of analytics. New reports denote that despite training programs, the talent is not enough to match the demand. The next step is to develop the right business process and framework which includes data infrastructure.
Simply combining technology systems along with the existing business operations isn't enough. For ensuring a successful transformation, all aspects of business activity need to be evaluated and combined to realize the full potential of data analytics.
Incorporating data analytics
The next generation of analytic tools will unleash even bigger opportunities. With new machine-learning, deep-learning and artificial-intelligence capabilities, an enormous variety of applications can be enabled which provide customer service, manage logistics and analyze data.
Technology and productivity gains seem an advantage, but also carry the risk of people losing jobs. A case of automation is the AI software developed by Bridgewater Associates, the world's largest hedge fund to improve efficiency.
With Data and analytics shaking up every industry, the effects will only become more noticeable as adoption reaches the masses.
As machines gain unprecedented capabilities to solve complex problems, organizations can harness these capabilities to create their unique value proposition and solve problems.
Now is the time to move beyond just collecting, storing & managing the data to take rapid actions on the continuous streaming data – Real-Time!!
- Digital makes customer self-service easy.
- Digitally engaged customers trust their utilities.
- Customer care, provided through digital technology, offers utilities both cost-to-serve efficiencies and improved customer intimacy.
- Digital technology brings the capability to provide more accurate billing and payment processing, as well as faster response times for changing addresses and bills, removing and adding services, and many other functions
- Using Mobile as a primary customer engagement channel for tips and alerts
- Predictive maintenance with outage maps and real time alerts to service engineer helps reduce the downtime and costs
- Smart meters allows utilities organizations to inform their customers about the energy consumption, tailor products and services to their customers while achieving significant operational efficiencies at the same time
- Minimize maintenance costs - Don’t waste money through over-cautious time bound maintenance. Only repair equipment when repairs are actually needed.
- Reduce unplanned downtime - Implement predictive maintenance to predict future equipment malfunctioning and failures and minimize the risk for unplanned disasters putting your business at risk.
- Root cause analysis - Find causes for equipment malfunctions and work with suppliers to switch-off reasons for high failure rates. Increase return on your assets.
- Efficient labor planning — no time wasted replacing/fixing equipment that doesn’t need it
- Avoid warranty cost for failure recovery – thousands of recalls in case of automakers while production loss in assembly line
TrainItalia has invested 50M euros in Internet of Things project which expects to cut maintenance costs by up to 130M euros to increase train availability and customer satisfaction.
- Identity: name, location, gender, age and other demographic data
- Relationships: their influence, connections, associations with others
- Current activity: orders, complaints, deliveries, returns
- History: contacts, campaigns, processes, cases across all lines of business and channels
- Value: which products or services they are associated with, including history
- Flags: prompts to give context, e.g. churn propensity, up-sell options, fraud risk, mood of last interactions, complaint record, frequency of contact
- Actions: expected, likely or essential steps based on who they are and the fact they are calling now
- All customer touch point data in a single repository for fast queries
- Next best actions or recommendations for customers
- All key metrics in a single location for business users to know and advise customers
- Intuitive and customizable dashboards for quick insights
- Real time hyper personalized customer interaction
- Enhanced customer loyalty
Customer 360º helps achieve Single View of Customer across Channels – online, stores, marketplaces, Devices – wearables, mobile, tablets, laptops & Interactions – purchase, posts, likes, feedback, service.
- Increased need & desire among businesses to gain greater value from their data
- Over 80% of data/information that businesses generate and collect is unstructured or semi-structured data that need special treatment
- Typically requires mix of skills - mathematics, statistics, computer science, machine learning and most importantly business knowledge
- They need to employ the R or Python programming language to clean and remove irrelevant data
- Create algorithms to solve the business problems
- Finally effectively communicate the findings to management
Any company, in any industry, that crunches large volumes of numbers, possesses lots of operational and customer data, or can benefit from social media streams, credit data, consumer research or third-party data sets can benefit from having a data scientist or a data science team.
- Kirk D Borne of BoozAllen
- D J Patil Chief Data Scientist at White House
- Gregory Piatetsky of kdnuggets
- Vincent Granville of Analyticsbridge
- Jonathan Goldman of LinkedIn
- Ronald Van Loon
- Smart Cars will communicate with traffic lights to improve traffic, find a parking spot, lower insurance rates based on telematics data
- Smart Homes will have connected controls like temperature, electricity, cameras for safety and watch over your kids
- Smart healthcare devices will remind patients to take their medication, tell doctors when a refill is needed & help curb diabetic attacks, monitor symptoms and help disease prevention in real time, including in remote areas
- Smart Cities & Smart Industries are the buzz-words in IT policies of many governments
- With sensors and IoT enabled Robots used in Manufacturing - new products could potentially cost less in the future, which promotes better standards of living up and down all household income levels
- Hyper-Personalization – with Bluetooth, NFC, and Wi-Fi all the connected devices can be used for specifically tailored advertising based on the preferences of the individual
- Real time alerts in daily life - The Egg Minder tray holds 14 eggs in your refrigerator. It also sends a wireless signal to your phone to let you know how many eggs are in it and which ones are going bad.
Now here are the Bad things:
- There are no international standards of compatibility that current exist at the macro level for the Internet of Things
- No cross-industry technology reference architecture that will allow for true interoperability and ease of deployment
- All the mundane work can be transferred to Robots and there is potential to loss of jobs
- All smart connected devices are expensive – Nest the learning thermostat cost about $250 as against $25 for a standard which gets a job done. Philips wireless controlled light cost $60 so your household will be huge expense to be remotely controlled
And the Ugly part:
- Remember the Fire Sale of Die Hard movie, a Cyber-attack on nation’s computer infrastructure - shutting down transportation systems, disabling financial systems and turning off public utility systems. Cyber-attacks can become common when devices are sold without proper updated software for connectivity
- Your life is open to hackers who can intercept your communications with individual devices and encroach your privacy. Imagine a criminal who can hack your smart metering utility system & identify when usage drops and assume that means nobody is home
- Imagine when you get into your fully connected self-driving car, and with some hacking a stalker’s voice come up from speaker “your have been taken” and you may not find Liam Neeson anywhere nearby, to rescue you.
The Internet of Things (IoT) concept promises to improve our lives by embedding billions of cheap purpose-built sensors into devices, objects and structures that surround us (appliances, homes, clothing, wearables, vehicles, buildings, healthcare tech, industrial equipment, manufacturing, etc.).
IoT Market Map -- Goldman Sachs
What this means is that billions of sensors, machines and smart devices will simultaneously collect volumes of big data, while processing real-time fast data from almost everything and... almost everyone!!!
IoT vision is not net reality
Simply stated, the Internet of Things is all about the power of connections.
Consumers, for the moment anyway, seem satisfied to have access to gadgets, trendy devices and apps which they believe will make them more efficient (efficient doesn't necessarily mean productive), improve their lives and promote general well-being.
Corporations on the other hand, have a grand vision that convergence of cloud computing, mobility, low-cost sensors, smart devices, ubiquitous networks and fast-data will help them achieve competitive advantages, market dominance, unyielding brand power and shareholder riches.
Global Enterprises (and big venture capital firms) will spend billions on the race for IoT supremacy. These titans of business are chomping at the bit to develop IoT platforms, machine learning algorithms, AI software applications & advanced predictive analytics. The end-game of these initiatives is to deploy IoT platforms on a large scale for;
- real-time monitoring, control & tracking (retail, autonomous vehicles, digital health, industrial & manufacturing systems, etc.)
- assessment of consumers, their emotions & buying sentiment,
- managing smart systems and operational processes,
- reducing operating costs & increasing efficiencies,
- predicting outcomes, and equipment failures, and
- monetization of consumer & commercial big data, etc.
IoT reality is still just a vision
No technology vendor (hardware or software), service provider, consulting firm or self-proclaimed expert can fulfill the IoT vision alone.
Recent history with tech hype-cycles has proven time and again that 'industry experts' are not very accurate predicting the future... in life or in business!
Having said this, it only makes sense that fulfilling the promise of IoT demands close collaboration & communication among many stake-holders.
A tech ecosystem is born
IoT & Industrial IoT comprise a rapidly developing tech ecosystem. Momentum is building quickly and will drive sustainable future demand for;
- low-cost hardware platforms (sensors, smart devices, etc.),
- a stable base of suppliers, developers, vendors & distribution,
- interoperability & security (standards, encryption, API's, etc.),
- local to global telecom & wireless services,
- edge to cloud networks & data centers,
- professional services firms (and self-proclaimed experts),
- global strategic partnerships,
- education and STEM initiatives, and
- broad vertical market development.
I'll close with one final thought; "True IoT leaders and visionaries will first ask why, not how..!"
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