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AI-Smart Assistants: A New Tech Revolution in the Make

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2018 has begun. And this year is going to witness a mega revolution in the field of technology – the rise of AI-powered digital assistants. Striking improvements in key technologies, like natural language processing and voice recognition are making smart assistants more productive, helping us use electronic devices just by interacting with them.

 
AI-Smart Assistants: A New Tech Revolution in the Make
 

Smart voice assistants are going mainstream. From Apple’s Siri to Google’s Assistant to Samsung’s Bixby, superior digital assistants are on a quest to make our lives easier, while taking us a step closer to a world where each one of us will have our own personal, 24/7 –all-ears AI assistants to fulfill our every wish and command.

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5G Mobile Innovation: 3 Key Takeaways That Every Business Leader Should Know About

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5G stands for fifth generation wireless connectivity based on the IEEE 802.11ac standard of broadband technology, though an official standard is yet to be fixed. It comes with a lot of promises – better bandwidth, faster speed and lower latency that affects (positively) customers and businesses, as well.

5G Mobile Innovation: 3 Key Takeaways That Every Business Leader Should Know About

Although it isn’t expected to roll out until 2020, a large number of companies have already started prepping up to adopt and incorporate 5G mobile connectivity into their business scopes and operations. As the biggest shift in technology is looming right ahead, business leaders around the world are leaving no stone unturned to fathom the rich impact of 5G on several next-gen techs, like self-driving cars and cloud computing.

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Chief Data Officer Is the Next “Commander” To Join the Digital Kingdom and Here’s Why

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Chief Data Officer Is the Next “Commander” To Join the Digital Kingdom and Here’s Why

An over-empowering digital transformation is here and it is wreaking havoc in the C-Suite. CDOs have started taking a front line in managing and pushing new tech like AI and machine learning to alter business landscapes forever.

As a matter of fact, this promising job title has existed for years, even decades – mostly in the financial market. But now when data is being generated at record high speeds, the job role of the CDO is emerging out bigger and better. No more a single person or a general crew is enough to tackle such challenging data issues – to fulfill complicated data management tasks, management is now looking up to specialized data experts.

Gartner predicts that 90% of multinational organizations will appoint a CDO by 2019. Though the first generation CDOs were only concerned about data governance and management, of late, they have been shifting focus on how to best implement data as the best strategic asset in organizations to trigger optimum results.  

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Take a look down to know how CDOs can add value to your organization, while streamlining data and developing strategies:

Be competitive, be ahead of the curve

The best way to ace is by taking over your competitors. In corporate jargon, it means to understand your competitor’s strategies better and arm yourself in the way. Also, it calls up to know your customers better, including the things they like to purchase and know ways you can fulfill their needs. Glean all of these observations with the flattering tool of IoT and machine learning, including social media and supply chain.

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Share information through Data silos

Think how would you feel if you are unable to share information within your department? It can be exasperating. But in reality, it happens. Employees working in the same company, even in the same team forget to share information – data is treated as a commodity that is traded for. That’s why, chief data officers break down data silos in an organization to make sure everyone within the framework get access to data to boost decision-making.

CDOs infuse life into data

All analysts are not good with data. No matter how much they pore themselves over into pie charts and bar diagrams, they just can’t nail it. Machine learning using Python and other related technologies has made things easier – now CDOs can infer trends and draw meaningful insights necessary for a better company future. And mind it these analyses eventually saves hours of production time, millions of losses and much more.

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There’s nothing better than cleaner, fresh data

Unkempt data is no data at all. In fact, data comes handy only when it is clean. Today, with the influx of so many data, organizations falter to keep pace with so much data extravagance data starts becoming dirty or of little use. This results in – every report run is full of flaws, estimates are wrong and lists compiles are inaccurate. As a savior in troubled situations, CDOs help in churning out crystal clear, consistent data by taking care of all the business processes, and making sure that they are properly maintained by the users.

CDOs are the meat and potatoes of C-Suite team

Not only they understand the intricacies of the subject matter, CDOs undoubtedly makes better use of your data, and looks forward to ways to use them in more meaningful manners. They are not here to hoard the data, but to share it extensively among the people working in the organization to produce fascinating results all around.  

Now that you know how important CDOs are, enroll for a reputable business analytics online certification by DexLab Analytics. Business analytics certification is the key to good times, go get one for yourself today!

 

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Google is Back in China! It Decides to Open an AI Lab in the Far-East

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Google is Back in China! It Decides to Open an AI Lab in the Far-East

 

Google is strengthening its artificial intelligence base, including China.

 

And it is so doing by establishing a new AI research center in Beijing. Google is digging deep into China, where it contravened the government in 2010 committing a spectacularly principled act of self-sabotage by refusing to self-censor search content and later found most of its services to be blocked. The company’s decision to return back to China is more about safeguarding its future, and acknowledging the supreme importance of technology’s most competitive field: AI.

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Bad Data is Really Bad for Machine Learning: Here’s Some Ways to Fix It

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Bad Data is Really Bad for Machine Learning: Here’s Some Ways to Fix It

The quality of data is the talisman of decision-making. Irrespective of the goals, the key to better decision-making lies in the quality of data. As it’s said, bad data takes its toll on organization’s data endeavors – as a result, only 25% of businesses are able to optimize the use of data for revenue generation, despite a volley of resources being thrown at them.

IBM has reckoned that bad data costs companies some $3.1 billion a year in the US alone, while as per Experian’s Data Quality survey, 83% of organizations alleged their revenue is affected by imprecise and incomplete customer or prospect data.

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R is Gaining Huge Prominence in Data Analytics: Explained Why

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Why should you learn R?

Just because it is largely popular..

Is this reason enough for you?

Budding data analytics professionals look forward to learn R because they think by grasping R skills, they would be able to nab the core principles of data science: data visualization, machine learning and data manipulation.

Be careful, while selecting a language to learn. The language should be capacious enough to trigger all the above-mentioned areas and more. Being a data scientist, you would need tools to carry out all these tasks, along with having the resources to learn them in the desired language.

In short, fix your attention on process and technique and just not on the syntax – after all, you need to find out ways to discover insight in data, and for that you need to excel over these 3 core skills in data science and FYI – in R, it is easier to master these skills as compared to any other language.

Data Manipulation

As rightly put, more than 80% of work in data science is related to data manipulation. Data wrangling is very common; a regular data scientist spends a significant portion of his time working on data – he arranges data and puts them into a proper shape to boost future operational activities. 

In R, you will find some of the best data management tools – dplyr package in R makes data manipulation easier. Just ‘chain’ the standard dplyr together and see how drastically data manipulation turns out to be simple.

For R programming certification in Delhi, drop by DexLab Analytics.

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Data Visualization

One of the best data visualization tools, ggplot2 helps you get a better grip on syntax, while easing out the way you think about data visualization. Statistical visualizations are rooted in deep structure – they consist of a highly structured framework on which several data visualizations are created. Ggplot2 is also based on this system – learn ggplot2 and discover data visualization in a new way.

However, the moment you combine dplyr and ggplot2 together, through the chaining technology, deciphering new insights about your data becomes a piece of cake.

Machine Learning

For many, machine learning is the most important skill to develop but if you ask me, it takes time to ace it. Professionals, who are in this line of work takes years to fully understand the real workings of machine learning and implement it in the best way possible.

Stronger tools are needed time and often, especially when normal data exploration stops producing good results. R boasts of some of the most innovative tools and resources.

R is gaining popularity. It is becoming the lingua franca for data science, though there are several other high-end language programs, R is the one that is used most widely and extremely reliable. A large number of companies are putting their best bets on R – Digital natives like Google and Facebook both houses a large number of data scientists proficient in R. Revolution Analytics once stated, “R is also the tool of choice for data scientists at Microsoft, who apply machine learning to data from Bing, Azure, Office, and the Sales, Marketing and Finance departments.” Besides the tech giants, a wide array of medium-scale companies like Uber, Ford, HSBC and Trulia have also started recognizing the growing importance of R.

Now, if you want to learn more programming languages, you are good to go. To be clear, there is no single programming language that would solve all your data related problems, hence it’s better to set your hands in other languages to solve respective problems.

Consider Machine Learning Using Python; next to R, Python is the encompassing multi-purpose programming language all the data scientists should learn. Loaded with incredible visualization tools, machine learning techniques, Python is the second most useful language to learn. Grab a Python certification Gurgaon today from DexLab Analytics. It will surely help your career move!

 

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Classifying Bank Customer Data Using R? Use K-means Clustering

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Before delving deeper into the analysis of bank data using R, let’s have a quick brush-up of R skills.

 

Classifying Bank Customer Data Using R? Use K-means Clustering

 

As you know, R is a well-structured functional suite of software for data estimation, manipulation and graphical representation.

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Facebook Shut Down AI amid Fears of Losing Control

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Facebook Shut Down AI amid Fears of Losing Control
 

Analysts at Facebook promptly shut down the Artificial Intelligence system over concerns they might lose control over the system. Recently, Facebook had developed a new Artificial Intelligence program, which could create its own language with the help of code words to make communication easier and effective. The researchers took it offline, when they understood the language used is no longer English.

 

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Though this isn’t the first time that AIs went a step ahead to take a different route instead of the oh-so-regular training in English language to develop their own more productive language, the recent Facebook incident made us wary about Elon Musk’s warnings about AI. “AI is the rare case where I think we need to be proactive in regulation instead of reactive,” Musk, co-founder, CEO and Product Architect at Tesla once stated at the meet of US National Governors Association. “Because I think by the time we are reactive in AI regulation, it’ll be too late,” he further added.

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Artificial Intelligence: Let’s Crack the Myths and Unfold the Future to You

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Artificial Intelligence: Let’s Crack the Myths and Unfold the Future to You

A lot of myths are going around about Artificial Intelligence.

In a recent interview, Alibaba founder Jack Ma said AI can pose a massive threat to jobs around the world, along with triggering World War III. The logic of shared by him explained that in 30 years, humans will be working for only 4 hours a day, and 4 days a week.

Fuelling this, Recode founder Kara Swisher vouched for Ma’s prediction. She supported him by saying Ma is “a hundred percent right,” adding that “any job that’s repetitive, that doesn’t include creativity, is finished because it can be digitized” and “it’s not crazy to imagine a society where there’s very little job availability.” 

Besides, I find all these stuffs quite baffling. I think that if AI is going to be the driving force towards innovation and bringing in a new technological revolution, it’s upon US to curate the opportunities that will require new jobs. Apocalyptic predictions just don’t help.

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Let’s highlight the myths and the logical equations:

Myth 1: AI is going to kill our jobs – it can never happen

Remember, it’s humans who have created robots. We excel at mechanizing, systematizing and automating. We spurred the automation drive, while infusing intelligence to the machines.

The present objective is to create AIs that can work together with human intelligence to develop new narratives for problems we are yet to solve. To solve these new problems, we need new kinds of jobs – there’s a great scope of opportunity, let’s not believe that AI will kill our jobs.

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Myth 2: Robots are AI. Not at all.

From drones to self-organizing shelves in warehouses to machines sent to Mars, all are just machines programmed to function.

Myth 3: Big Data and Analytics are AI. Who said that?

Data mining, Data Science, Pattern Recognition – they are just human-created models. They might be intricate or complicated in nature, but not AI. Data and AI are two entirely different and divergent concepts.

Myth 4: Machine Learning and Deep Learning are AI. Again a big NO.

Though Machine Learning and Deep Learning are a part of the enormous AI tool kit, they are not AI. They are just mere tools to program computers to tackle complex patterns- like the way your email filters out spam by “understanding” what hundreds and thousands of users have identified as spam. They look uber smart, undeniably, in fact scary at times, when a computer wins against a renowned expert at the game GO, but they are definitely not AI.

Myth 5: AI includes Search Engines. Definitely NO.

Search Engines have made our lives easier, undoubtedly. The way you can search information now was impossible few years back, but being the searcher, you too contribute the intelligence. All the computer does is identify patterns from what you search and suggest it to others. From a macro perspective, it doesn’t actually know what it finds because it’s dumb in the end. We feed them intelligence, otherwise they are nothing.  

So, instead of panicking about the uncertainties that AI may bring into our lives, we should take a bow and appreciate the efforts humans gave into creating something so huge, so complex like AI.

And remember, AI has always created jobs in the past and didn’t take them. So, be hopeful!

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