Subscribe to our Newsletter | To Post On IoT Central, Click here

artificial intelligence (4)

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.

Read more…

We are living in a century where technology dominates lifestyle;Digital Transformation with Big Data, IoT, Artificial Intelligence(AI) are such examples.
Over the past six months, Chatbots have dominated much of the tech conversation, the next big gold rush in the field of online marketing.
Chatbots are built to mimic human interaction, making them seem like an actual individual existing digitally. It could live in any major chat product (Facebook Messenger, Slack, Telegram, Text Messages, etc.), powered by basic rules engine or NLP and AI.
Chatbots have helped in conversation commerce in real time such as booking a cab or ordering a bouquet of flowers or pizza. Consumers will benefit from chatbots through personalization, and this is where social media plays a big part.

Here are a couple of other examples:
·       Weather bot: Get the weather whenever you ask like Poncho
·       Grocery bot:  Help me pick out and order groceries for the week like Yana, MagicX
·       News bot:  Ask it to tell you whenever something interesting happens like TechCrunch, CNN
·       Personal finance bot: It helps me manage my money better like Abe
A chatbot for an airline will function fundamentally differently from a banking bot.
People are now spending more time in messaging apps than in  social media and that is a huge turning point. Messaging apps are the platforms of the future and bots as over 90% of our time on mobile is spent on messaging platforms like Facebook messenger, Whatsapp, Wechat, Viber etc.
Typically business need to answer following questions to create a bot:
·       Do you need a constant communication back and forth with the consumer?
·       What are customers’ expectations for the interaction?
·       How will the bot act?
·       What happens when the bot fails?
Chat bots have to be great at answering questions, this is usually how they are challenged, and IBM’s Watson is probably the best question and answer system.
There are several advantage of Chatbots:
·       24×7 availability – A bot exists digitally unlike a human being, and can thus be pressed into service continuously without any interference
·       Faster response time than humans, coupled with an AI, chatbot’s machine learning and multi-tasking abilities make it a highly efficient virtual assistant
·       Bots allow for a two-way, personalized interaction between the consumer and a brand
·       Saves Resources – Employing a chatbot to handle basic customer interactions can free up valuable human resources without a decline in productivity
Tacobot Allows to order Taco Bell even more quickly.

KLM has a customer service bot that's able to check your flight status and let you know if it's been delayed.

Interacting with software at a human level is becoming more mainstream from digital assistants  like Google Home, Google Now, Apple Siri. 

Going forward people will not be able to tell the difference between human and machine.
Read more…
Today, with Digitization of everything, 80 percent the data being created is unstructured. 
Audio, Video, our social footprints, the data generated from conversations between customer service reps, tons of legal document’s texts processed in financial sectors are examples of unstructured data stored in Big Data.
Organizations are turning to natural language processing (NLP) technology to derive understanding from the myriad of these unstructured data available online and in call-logs.
Natural language processing (NLP) is the ability of computers to understand human speech as it is spoken. NLP is a branch of artificial intelligence that has many important implications on the ways that computers and humans interact. Machine Learning has helped computers parse the ambiguity of human language.
Apache OpenNLP, Natural Language Toolkit(NLTK), Stanford NLP are various open source NLP libraries used in real world application below.
Here are multiple ways NLP is used today:
The most basic and well known application of NLP is Microsoft Word spell checking.
Text analysis, also known as sentiment analytics is a key use of NLP. Businesses are most concerned with comprehending how their customers feel emotionally adn use that data for betterment of their service.
Email filters are another important application of NLP. By analyzing the emails that flow through the servers, email providers can calculate the likelihood that an email is spam based its content by using Bayesian or Naive based spam filtering.
Call centers representatives engage with customers to hear list of specific complaints and problems. Mining this data for sentiment can lead to incredibly actionable intelligence that can be applied to product placement, messaging, design, or a range of other use cases.
Google and Bing and other search systems use NLP to extract terms from text to populate their indexes and to parse search queries.
Google Translate applies machine translation technologies in not only translating words, but in understanding the meaning of sentences to provide a true translation.
Many important decisions in financial markets use NLP by taking plain text announcements, and extracting the relevant info in a format that can be factored into algorithmic trading decisions. E.g. news of a merger between companies can have a big impact on trading decisions, and the speed at which the particulars of the merger, players, prices, who acquires who, can be incorporated into a trading algorithm can have profit implications in the millions of dollars.
Since the invention of the typewriter, the keyboard has been the king of human-computer interface. But today with voice recognition via virtual assistants, like Amazon’s Alexa, Google’s Now, Apple’s Siri and Microsoft’s Cortana respond to vocal prompts and do everything from finding a coffee shop to getting directions to our office and also tasks like turning on the lights in home, switching the heat on etc. depending on how digitized and wired-up our life is.
Question Answering - IBM Watson is the most prominent example of question answering via information retrieval that helps guide in various areas like healthcare, weather, insurance etc.
Therefore it is clear that Natural Language Processing takes a very important role in new machine human interfaces. It’s an essential tool for leading-edge analytics & is the near future.
Read more…

Upcoming IoT Events

6 things to avoid in transactional emails

transactional man typing

  You might think that once a sale has been made, or an email subscription confirmed, that your job is done. You’ve made the virtual handshake, you can have a well-earned coffee and sit down now right? Wrong! (You knew we were…


More IoT News

IoT Career Opportunities