Difference Between AI, Machine Learning, and Deep Learning

                 Artificial Intelligence is, locally, a computer algorithm tasked with solving input problems based on accessible data and operational parameters, with respect to the amount of computational power available to the algorithm. More generally, AI is the name given to machine intelligence.

With the vast field of AI are specific concepts like machine learning and deep learning.
In the same way as Russian Matryoshka dolls where the small doll is nested inside the bigger one, each of the three segments (Deep Learning, ML and AI) is a subset of the other. Advances in these three technologies are already revolutionizing many aspects of modern life, and although very much related, they are not the same.
In this post, we'll begin with the biggest doll "AI" and work our way down to the smallest.

Read More....

The Real Threat of Artificial Intelligence

Too often the answer to this question resembles the plot of a sci-fi thriller. People worry that developments in A.I. will bring about the "singularity" - that point in history when A.I. surpasses human intelligence, leading to an unimaginable revolution in human affairs. Or they wonder whether instead of our controlling artificial intelligence, it will control us, turning us, in effect, into cyborgs.

These are interesting issues to contemplate, but they are not pressing. They concern situations that may not arise for hundreds of years, if ever. At the moment, there is no known path from our best A.I. tools (like the Google computer program that recently beat the world's best player of the game of Go) to "general" A.I. - self-aware computer programs that can engage in common-sense reasoning, attain knowledge in multiple domains, feel, express and understand emotions and so on.

This doesn't mean we have nothing to worry about. On the contrary, the A.I. products that now exist are improving faster than most people realize and promise to radically transform our world, not always for the better. They are only tools, not a competing form of intelligence. But they will reshape what work means and how wealth is created, leading to unprecedented economic inequalities and even altering the global balance of power.

It is imperative that we turn our attention to these imminent challenges.

What is artificial intelligence today? Roughly speaking, it's technology that takes in huge amounts of information from a specific domain (say, loan repayment histories) and uses it to make a decision in a specific case (whether to give an individual a loan) in the service of a specified goal (maximizing profits for the lender). Think of a spreadsheet on steroids, trained on big data. These tools can outperform human beings at a given task.

This kind of A.I. is spreading to thousands of domains (not just loans), and as it does, it will eliminate many jobs. Bank tellers, customer service representatives, telemarketers, stock and bond traders, even paralegals and radiologists will gradually be replaced by such software. Over time this technology will come to control semiautonomous and autonomous hardware like self-driving cars and robots, displacing factory workers, construction workers, drivers, delivery workers and many others.

Unlike the Industrial Revolution and the computer revolution, the A.I. revolution is not taking certain jobs (artisans, personal assistants who use paper and typewriters) and replacing them with other jobs (assembly-line workers, personal assistants conversant with computers). Instead, it is poised to bring about a wide-scale decimation of jobs - mostly lower-paying jobs, but some higher-paying ones, too.

This transformation will result in enormous profits for the companies that develop A.I., as well as for the companies that adopt it. Imagine how much money a company like Uber would make if it used only robot drivers. Imagine the profits if Apple could manufacture its products without human labor. Imagine the gains to a loan company that could issue 30 million loans a year with virtually no human involvement. (As it happens, my venture capital firm has invested in just such a loan company.)

We are thus facing two developments that do not sit easily together: enormous wealth concentrated in relatively few hands and enormous numbers of people out of work. What is to be done?

Part of the answer will involve educating or retraining people in tasks A.I. tools aren't good at. Artificial intelligence is poorly suited for jobs involving creativity, planning and "cross-domain" thinking - for example, the work of a trial lawyer. But these skills are typically required by high-paying jobs that may be hard to retrain displaced workers to do. More promising are lower-paying jobs involving the "people skills" that A.I. lacks: social workers, bartenders, concierges - professions requiring nuanced human interaction. But here, too, there is a problem: How many bartenders does a society really need?

The 5 things that changed this year to drastically improve your productivity and happiness

1. Life scrum board.

That's right, I use agile (Trello to be precise) to organize my week/month into:

• Backlog (never ending to-do list)
• This Week (must get done)
• Today (goals for the day)
• Done (Halleluja, let's hope the task stays there)
• Trashed (because priorities and plans change).

I assign coloured labels for : business, content creation (youtube, etc.), fitness, family, and errands. This makes it easy to visually see that the week is balanced.

2. Mini awesome-ness journal.

Tony Robbins always says :

You can't be grateful and angry/stressed at the same time.

He is absolutely right. Practicing gratefulness is really making an impact on me, although it can sometimes be easy to forget to be grateful throughout the day. A sure-fast way I go with is taking a few minutes right after waking up, and right before going to sleep.

In the morning I note 2 or 3 things that: I'm grateful for, that I will make me grateful if they happen that day. In the evening, I reflect and write down 2 or 3 things that: were awesome, I could have made better, and will certainly make better the next day.

3. No iPhone mornings.

I used to be the kind of person that checked messages and e-mails first thing in the morning, but ever since I banned myself from checking them for the first hour of my day (about a year ago), everything has changed (and nothing bad happened as a result of that)! Focusing on myself first thing in the morning, doing things like stretching, cardio, relaxing with tea while I work on a project near and dear to my heart, and setting goals for myself for the day are typically what I do. The change has been incredible.

Starting your day off by responding to what other people want sets you on a stressful path for the day where your own goals are secondary.

Focusing on your own goals and needs in the morning (even if that means waking up earlier), is sure to make the rest of your day a whole lot calmer and productive as you already took care of the most important things: you and your goals.

4. I take productive day-vacations.

Entrepreneurs, part-time or full-time, are known for being happy to work 16 hr days 7 days a week to avoid working an 8 hr day 5 days a week. But that doesn't mean we are sitting in front of the laptop coding or designing or making sales calls all day every day - at least it shouldn't look that way.

When you're working so much, even when it's something you love, you need proper downtime without compromising what you're working for.

Meaning? Travel! Go out! Take a drive outside the city! And when you're in the train/bus/boat/car/plane, use that commute time to whip out something amazing and refreshed on your laptop.

When you get to the location, network and learn from others (what better way to test your latest idea, or workout a problem you're working on than with real people?) and your surroundings.

And in the evening? Get yourself a massage, go steam, and watch the stars. An hour or two of completely unplugging from the hustle is guaranteed to centre you, give you clarity, and get new juices flowing.

The chances that your servers will go down, or your number one client will have an emergency needing your immediate attention, are low. And, everything you'll get from that mini vacation will be just what you need to crush another week of hustle.

5. Apply the 80-20 rule to everything.

Consuming vs. creating is the latest place that I'm finding this rule extra helpful. It's easy to get caught up in being super productive, creating/building all day/night long, just as easy as it is to spend hours bouncing between blog posts and videos.

80% Creating, and 20% Consuming

Aim to spend 80% of your time building and creating, and 20% of your time consuming things that will help you 1. be more creative when you get back to building, and 2. see where what you're creating fits in with what everyone else is consuming.

These 5 things have really helped me in the last year to reduce stress, accomplish more, and most importantly, feel great more of the time.

Industrial Robotics Market Highest Demand in Core Industry by 2024

Industrial Robotics Market size is expected to witness an exponential increase in growth over the forecast period. Benefits such as improved productivity, cost reduction, and demand from emerging countries are expected to drive the industrial robotics market over the forecast time frame.

Use of labor has been replaced with self-programming robots owing to cost-effectiveness and efficiency due to their capacity to understand the environmental changes through the addition of advanced sensors. The increase in measures owing to the safety rights of labor is also expected to expand the industrial robotics market growth.

Common Myths Around Virtualizing Big Data

Big data burst on to the scene a little over a decade ago. Today it is not an obscure term confined to just a handful of bleeding edge companies. It is a mainstream trend that every enterprise undergoing a digital transformation journey has adopted. The technology landscape around big data has broadened dramatically; in the early days it meant Apache Hadoop, today it includes Apache Spark and NoSQL databases like MongoDB and Apache Cassandra among many other new technologies.

Myth 1: Virtualizing big data applications is fine for development but not for production

It is true that software engineers have used virtual infrastructure to develop big data applications over the last several years. However, these big data applications have now also made their way into production. Virtualized applications make it possible for various users, including business analysts and data scientists, to work on different data analysis tasks simultaneously, resulting in significant productivity increases of these teams

Myth 2: There’s a performance penalty when virtualizing Hadoop

Misperceptions about the performance of virtualized Hadoop still remain, but it should be a moot point by now. Since 2011, performance benchmarks have consistently shown that running Hadoop on virtual machines is as performant, or more, as running Hadoop on physical machines, with results showing that Map Reduce jobs completed up to 12 percent faster and Spark/Machine Learning jobs up to 10 percent faster. The latest performance benchmarks, performed by VMware in 2016, show that Hadoop scales amply on virtual machines with similar overall performance to bare metal and distinct advantages when it comes to utilization of cluster resources.

Myth 3: You need a SAN for virtualizing Hadoop, but can Hadoop even use a SAN?

These myths are related, so let’s tackle them both. First of all, it is a misperception that the basic features of virtual machines require a SAN. It is common for enterprises to use non-shared direct-attached storage to host Hadoop data in the virtual machines attached to that storage. Vendors in the space both support and often recommend direct-attached storage for performance benefits and cost savings.

Secondly, if you want to take advantage of shared storage solutions like a physical SAN or virtual SAN such as VSAN, then Hadoop not only works, but many users prefer to use a SAN to begin their first Hadoop experiments, often because it is a core part of their infrastructure, and was in place when the enterprise first started to adopt Hadoop.

Myth 4: You can only run the traditional Hadoop stack but not the latest and greatest tech

In many ways Hadoop has become a catchall for big data, but it is a misleading one. At its outset just over a decade ago, Hadoop meant Hadoop Distributed File System and several other tools to consume data from it like MapReduce, Hive and Pig. Today, it encompasses many projects, with other big data projects often dragged into the net. However, Apache Spark is distinct from Hadoop (although it integrates with it), and offers faster and more efficient means to analyze ever-growing volumes of data. The performance benchmark paper cited earlier shows comparable performance between Apache Spark running on virtual machines or bare metal.

It is also not only possible, but common, to find enterprise users running different versions of Hadoop and Spark from multiple Big Data vendors in separate clusters running on virtual machines within the same grouping of hardware.

Myth 5: The hot tech is containers so you should use that instead of VMs

Container technology like Docker is white hot at the moment, and for good reason. It is becoming a popular choice with cutting edge developers because it is easy to use and lightweight. They have quickly become standard operating procedure in many development houses. However, it is important to understand the right use cases for using containers with a big data strategy. Containers are best suited to hold the Compute side of Hadoop – the part that executes your algorithms, such as the NodeManagers of YARN and Executors of Spark. Containers require you to separate out your data storage to a different place. Holding terabytes of data in a container is not the accepted wisdom today. So when applying virtualization to this, the containers are executing in a virtual machine, either one to one with the virtual machine or one to many, where the data is retrieved by the VM. If high levels of security are an enterprise focus then isolation of concerns and users is more optimal through virtual machines. The combination of virtual machines and containers brings mature operations management to the challenges of handling containers in production.

Big data can lead to effective business process reformation

If there is an oft-cited and “classic” example of big data use, it’s the story of an enterprise that capitalizes on parsing and analyzing unstructured and semi-structured data about its customers from the Internet and other data sources that formerly went unnoticed. But the other part of big data that is “big” is effective use of structured data that comes in from systems of record like customer master files, order and shipping files, and even financial charts of accounts. If you look at the historical accumulation of this system of record data, it is “big” in the sense of the volume it presents. It remains a largely untapped data resource that traditional corporate reporting only scratches the surface of.

System of record

The other element of big data that doesn’t get talked about as much as breakthrough information producing competitive advantage, is the operational agility that effective big data analytics can produce. It is the structured, system of record, data that contributes the most to this agility – and its contribution is vested in the reformation of business processes that can be tuned for better performance.

Here are some current business process headaches that poor system of record data in companies creates:

Part descriptions initially entered by the company’s engineering department do not reflect the nomenclature that is used in the field, so someone has to manually go in and change them. Meanwhile, service reps have a hard time determining the correct parts to use in their daily work.

Part and assembly revision levels are difficult to synchronize for an aerospace company that must maintain three different sets of part and assembly levels – one that is internal, one that reflects the original part numbers from OEMs, and one that reflects part and assembly numbers that military customers want assigned.

Web Analytics tools

For deep insights into how users interact with your website or app, look no further than these affordable tools

Analytics tools have evolved considerably since the early days of the internet, when web developers had little more than hit counters to work with. And as the internet continues to evolve, analytics tools will continue to change, giving us greater insight into how our audience uses and interacts with our apps and websites.

• Google Analytics
• Kissmetrics
• Mixpanel
• Localytics
• Segment
• Spring Metrics.
• Woopra.
• Clicky.
• Mint.
• Chartbeat.
• UserTesting
• Crazy Egg
• Mouseflow

Read  for more details........

Big Data: Managing the compliance risks

Companies can often leap into big data and analytics projects without always having a solid understanding of the compliance requirements for the data they want to collect and process.

This is particularly true of smaller companies which are less likely to have sufficient knowledge of the issues around personally identifiable information (PII), or the necessary resources in place to manage them.

Even in cases where in-house IT teams do have that knowledge, other employees may not fully understand the requirements and could start to analyse or process information outside of the original remit of the project. Data gathered for HR processes, for example, could be taken and repurposed for supporting automated decisions that directly affect staff, such as hiring, promotion or termination.

With locations around the world, larger companies do not escape these problems, in fact, they grow in complexity. The larger the data set being analysed, the larger the costs associated with the compliance requirements.

Even in the EU, where there seems to be a single set of regulations, each state has interpreted them in a different way. A data controller needs to understand these locale-specific rules and not just the EU-wide requirements.

Read more...

Arduino based Multi-Functional Robot

This is an arduino based multifunctional robot with features like; live video feed on a robot arm, object/body tracking with ultrasonic sensors, remote controlled, sound triangulation and sound effects, multi-directional movement with the use of mecanum wheels and much more.

5 THINGS YOU CAN LEARN FROM COMPETITOR ASO ANALYSIS

1) User Demographics

What?
User demographics are data related to age, gender, income and education level.

Why is this important?
Demographics have been known to most influence the design, features and app functionality.

What can my competition teach me?
Peanut butter and jelly (bare with me here): a sandwich that formed our childhood (and adult life). Producers or peanut butter would be interested in knowing the demographic stats for the jelly company. Complementary products can learn from each other as much as competing products. By understanding age groups of competitor or complementary products, you may think of altering your marketing strategy and even the product. In what regards to the app, publishers choose to change designs and user interface depending on age groups and gender – would you want to add some more pink in your app because the market is shifting to a pink-loving demographic?

2) App Intelligence

What?
App intelligence provides information about market shares and revenues earned.

Why is this important?
In theory, you are trying to get a piece of the pie from app stores. But do you know how big the pie is, or better still if there is a pie at all?

What can my competition teach me?
Using the food industry again as an example, how would McDonald’s find out if a market is suitable for them or not? They may monitor profits of a competitor, like Burger King, in the specific region to decide if they can be a direct competitor or not. Just the same, app intelligence is extremely useful for understanding the performance of direct competition in specific markets and stores.

With both stores giving you revenue estimate for your app, it is equally important to know if your competitor is making as much (or as little) profit as you are. Insight on competitors’ profits by store can help you adjust your mobile marketing strategy to target different stores. For example, if you find that there is no strong competition on Google Play, you may want to shift some more budget towards your Android app.

3) Category Analysis

What?
App stores allow you to classify your app under their preset categories to join similar apps in the same pool.

Why is this important?
Other than overall ranking, stores rank apps under their relevant category. This means that you will be working on improving your rankings within the vertical category you are in.

What can my competition teach me?
Both Google Play and Apple store require you to select the category that best describe your app, and both have their own tricks for ASO. While Google Play offers you one choice between 28 categories, Apple Store offers you the chance to select two out of 25 options. This give you double the chance to be a leader in your vertical category.

What can you gain from looking at competitors? By monitoring which categories competing apps you can better position yourself in the category that users expect to find you! Take a sports shop, for example. As an mCommerce app, it can be categorized under Shopping. However, by monitoring competitors, the shop found out that their competitor is ranking higher due to people searching the Sports section to find mCommerce apps related to their favorite hobby.

4) Keyword Analysis

What?
Keywords are found in the app description and are used to rank you app.

Why is this important?
Keywords are one of the major store ranking factors and help determine search relevancy.

What can my competition teach me?
Where monitoring a competitors category can help you rank better within that specific group, keyword monitoring can help you track & optimize your ranking in specific search results. Much the same as SEO, understanding both the volume and hits for your app store keywords can help you optimize the words you choose (ie. by choosing synonyms).

You can also get a leg-up on your competition if you see that certain keywords are not being used in their app name, app pitch and full description. Including the relevant keyword in any of these three places can push you ahead of competitors who haven’t done their ASO homework.

5) Reviews and Ratings

What?
An app review is a comment a user who downloaded the app can leave on the store. It will be publicly shown regardless of the

Why is this important?
Reviews affect your rankings, help promote your app, inform you of bugs, collect suggestions and is another way to communicate with your users.

What can my competition teach me?
How can reading competitor review help you with your app, you ask? Reading user reviews of competitor apps can help you gain insight on what users want from the app, what they aren’t getting, and what type of language they are using (which can inform your keyword strategy). Once you know what users like or don’t from your competitor’s app, you know how to better position your own app to that target user. When it comes to features, if you have what your competition doesn’t, give users what they are looking for, and update your keywords accordingly!

Why robust data infrastructure is key to digital government

Analytics projects are expensive and difficult. There are two fundamental requirements for undertaking data analytics projects: clean, integrated, meaningful data, and skilled resources in the form of specialist data scientists.

In recent years, we have seen industries take huge leaps of faith, pouring significant investment into analytical projects with mixed results. Trying to make sense of bad quality data that is disjointed, siloed and filled with redundant and obsolete information can, at best, create huge delays in getting meaningful results or, at worst, drive wrong decisions through misleading correlations and trends.

In most instances, organisations don’t know what value they will get from analytics until the analysis is actually done. This makes it extremely difficult to construct a business case for investing in analytics projects in advance.

Read more....

Potential Growth Of IoT (Internet Of Things) Chip Technology In Industries (2017)

The IoT chip technology constitutes hardware such as processors, sensors, connectivity ICs, memory devices, and logic devices which are used in IoT-enabled devices. 


The IoT chip market is expected to reach USD 14.81 Billion by 2022 from USD 5.75 Billion in 2015, growing at a CAGR of 13.2% during the forecast period.

All the major end-use applications, automotive and transportation held the largest share of the IoT chip market in 2015.

The major drivers for the growth of the IoT chip market are the increasing demand for application-specific MCUs and flexible SoC-type designs and increasing investments by major giants of this industry in the IoT market.

The IoT chip industry for the retail end-use application is expected to grow at the highest CAGR from 2016 to 2022, followed by wearable devices.

For complete information read this latest article,