AI and the Rise of Robotics

How AI will lead to the era of Robotics

The concept of robots has been around for so long that it precedes the roots of most modern technology. Automated machines that are capable of doing more menial tasks could be dated back to as early as 4th century BC with steam-powered automatons doing menial tasks.

Back then scholars and philosophers like Homer saw robots as a means of human salvation as it presented the possibility of human equality, slavery being a huge issue at the time. However, a couple of millennia later the take on robotics had a slightly darker undertone. Works of post-industrial novelists such as James Orwell and movies such as ‘The Terminator’ depicted robotics as the end of mankind.

Even early 20th-century technology experts believed that robotics could lead to massive layoffs and could destroy working-class communities and they were not wrong. The first sign of mainstream robotics came in the early 70s when hydraulic arms started taking over production lines at a car and heavy machines factories. Cities such as Detroit and Munich suffered massive layoffs. Yet the use of automated machinery continues with several verticals such as AI built around them.

AI in robotics

The State of Robotics Today

Robots have come a long way from their early hydraulic single motion ancestors and are surprisingly doing a lot a lot of things that most humans could only dream of achieving in their entire lifetimes. From Toyota’s Kirobo having a conversation in space to Sophia’s Saudi Arabia Citizenship, robots are going places and bringing certain science fiction theories that were once dismissed as hogwash, to life. With the exception of breaking Asimov’s three laws of course. Nonetheless, robotics is going through huge advances today especially with technologies like AI and Machine Learning catching up quite fast. Certain industries have become so accustomed to robotics that industry veterans now wonder how they survived without them. So, let us take a closer look at the some of those things.

 

1. The Space Bots:

When the seven Mercury Astronauts were under the threat of being replaced by a monkey, the last thing they would’ve been thinking would be that after 50 years people have to go through the same thing with robots. Well even if they did think that, they would not have been wrong. Because today most of the transplanetary missions are being carried out by rovers and the robots aboard the ISS are beginning to function more and more like their human counterparts. For astronomers and researchers, it is truly hard to imagine sending another human to the moon let alone to Mars.

So, in that respect robots have allowed us to go farther than we would have ever imagined possible and they aren’t just there planting a flag, instead they are drilling on its surface, running tests and sending in chunks of data that would have taken a human a lifetime to collect.

2. Drones:

There is no better field to measure the impact of robotics than the defense sector. Robots have been a huge part of many important military operations in the past two decades. Particularly the Drones guided by AI are capable of flying, targeting and even firing from long range as a staple of the U.S. air force.

However it is not just the terminator style drones that are making the headlines, but recent years have also seen a hike in the number of shopping drones and transport drones. The use of flight-capable drones guided by AI could be argued as one of the largest prospects for the retail industry.

3. Transportation:

Automated transport is a field that has been picking up pace off late. Self-driven cars, locomotives and aircrafts are thought by many experts to be the future of transportation. Every day, AI advancements in transport is showing promising results that could in the near future be translated to mainstream modes of transport.

4. Machine Learning:

One of the most recognizable features in robotics today is the technology known as machine learning. Machines and programmes that are capable of analyzing various patterns of the tasks that they are assigned to and create their own set of algorithms to function around more effectively. The introduction of machine learning to robotics has been one of the largest leaps in the industry. Humanoid robots are making a mark in several areas of the industry with many acting in movies, working as astronauts, therapists, nurses, yes that’s right nurses! Soon the caring feminine touch will be replaced by the cold metal claws of a robot named after the tiny metal gerbils from ‘Thundercats’.

 

Is the ‘Storm’ really Coming?

Well back in 1984, that line could be dismissed as James Cameron just being crazy but today we are really not in a position to tell. As a matter of fact, something big is bound to happen by the year 2029. Not on Skynet proportions but more on the lines of an AI-based global network that will function all by itself. Coming to think of it that sounds exactly like Skynet but on a more nerdy tone.

Levity aside in the decade to come organizations like SpaceX, Tesla and Google are going to put most of their resources into developing AI technology to such an extent that total automation would be possible. Elon Musk’s dream of setting up a space colony on Mars revolves largely around the prospect of AI technology that would help us from the designing phase of the space crafts to the setting up of habitats on the red planet.

Backed by AI, the possibilities of advancements in robotics are endless. Even the hardware capabilities are going through an overhaul with robots now being equipped to mimic human-like body physics. At this point the possibilities seem endless, but, only time can tell how far we can go with this.   

Tips to get you started with your UX user research

What is User Research and Why should you do it?

Research, as we all know, is a crucial aspect of any undertaking, be it shopping for clothes or building your own backyard nuclear-powered submarine. Especially if you are a UX designer, research is quite important to understand the what people want from the product that you design. Without a clear understanding of what your users’ needs are and what they expect, you might as well be throwing darts in the dark. Unlike designing, user research is almost a never-ending process which needs to be carried out throughout the product’s lifecycle. However, in this article, we will focus on the user research for designing and what you need to know when you are starting out.

UX research strategy

Where should you start?

There are a lot of points from where you can start your research depending on the nature of your product. A hypothesis of your current objectives will provide a picture of the goals you seek to achieve through the research.  

1. Introspection

Before starting the research process itself it would be a good idea to try and define your product, the basic function it would serve and the demographic it is intended for. At this point, discovering the problems users presently face in the market would help define your products’ purpose. Analysing the market demand and your chances of proliferation would also be a wise strategy at this stage because going back to the drawing board because of an overcrowded market is never a good contingency.

2. Conceptualization    

Identify various business demographics that you feel might be interested in your product and understand their real-time user goals. Conducting surveys, interviews, focus groups etc. are among the many methods that are commonly used. The data achieved through research can be broadly classified between qualitative, quantitative, attitude and behavioral. Using the all the information and feedback thus received to create a rough iteration of the design.

 

3. Research Methods

  • Surveys

Surveys are often an easy way to start out with UX research. With online surveys being a huge trend today, it is not hard to find participants. However, the ease of finding participants hardly makes up for the level of inaccuracy involved in this process as most people tend to give biased responses. Short surveys with simple, well-worded questions involving both open-ended as well as multiple choice questions tend to work best with this method.

  • Interviews

Interpersonal Interviews are one of the oldest, tried and tested methods of understanding users and getting to know them. Interviews offer a great deal of flexibility when it comes to questions and ensuing discussions that could lead to a higher level of revelation. Compiling a valid set of questions and putting them through a trial run before the interview to check their feasibility will help. As always the interviewer must remain calm and composed and make the user feel comfortable before the interview. Engaging the user to get the most out of them would be a good idea.

  • Focus Groups

Engaging a small group of people in a discussion on your product and its potential can bring out valuable insights and ideas. However, this process has a good chance of backfiring as groups tend to follow a herd mentality eventually leading to supporting the popular opinion rather than their own which ultimately can produce inaccurate data. Allowing each person in the group to express themselves properly is the key to success when dealing with focus groups.

  • Usability Testing

One of the most effective methods of UX research is usability testing. This method involves putting the target users through a set of tasks designed to demonstrate the features and functionality of your product. This process can be leveraged to derive valuable inputs. Although not as elaborate as with questionnaires, usability testing provides the most accurate data and feedback. There are several methods of usability testing as well as tools that can aid in carrying out the processes.

4.Analysis

The data derived from these research methods can be analyzed and compared with your initial hypothesis. By doing so you will get an idea of the changes that need to be done. One of the advantages of UX user research is that it can be done at any stage in the development process and it’s up to you to choose the most valid method at your current stage.

Conclusion

When it comes to UX it is easy to lose your way with designs and most of the time designers end up designing the product with their own needs in mind and that of their investors as well. Research here can offer a neutral perspective and bring out the best ideas that users can identify with. While each method has its own advantages it would be ideal to use them in combinations to paint a wholesome picture.

5 Reasons to use Apache Cassandra database

Advantages of Apache Cassandra Database

As one of the better-known NoSQL database, Apache Cassandra is fast becoming a preferred database of enterprises and SME’s alike. Its robust performance in applications needing heavy write systems traversing massive volumes of data is what makes it stand apart from its contemporaries.

A typical Cassandra database consists of keyspace (similar to a schema in a relational DBMS), column families (consistent with a table in a relational DBMS), and rows/columns. It also utilizes a Cassandra Query Language (just like SQL) to retrieve records, carry out actions, and communicate with the Cassandra database.

Apache Cassandra

 

Here are five reasons why Cassandra makes for a great database system

1. No single point of failure

Its masterless architecture makes Cassandra highly fault tolerant. Because of this, any downtime affecting a few nodes will not impact the overall performance of the system.  This enhanced fault tolerance level is a great draw for enterprises who wish to provide ‘always-on’ online services to their customers.

If we look beyond a single datacenter then Cassandra can be of great help. It allows seamless replication of the data center, thus facilitating a strong disaster recovery and backup/retrieval system within your organization.

2. Handling massive datasets made easy

Hulu, NetFlix, Instagram, and Apple, the list of enterprise users who benefit from Cassandra speak a lot about its capability to handle humongous volumes and variety of data. If your organization too faces a probability of data volume expanding exponentially and scaling up at a rapid rate then you need not look beyond Cassandra.

You can rely on Cassandra to continue delivering optimized performance without any impact of the huge rapid change in the data it is handling

3. Logging is simplified

In today’s homogenous environment, a typical company has to deal with multiple clients and servers (Android, web, iOS to name a few). In such an environment, logging and analyzing logs become huge challenges to deal with. Cassandra comes across as a viable solution to centralized logging.

This way, your development team need not spend a lot of time on logging and can instead focus on better product development.

4. Fast reads and superfast writes

Workloads like metrics collection and logging need extremely fast writes for optimal performance. This is where Cassandra scores heavily over its peers. It offers a scalable read-write performance. This means that if you know a single server’s write performance, you can accurately assess how many servers are needed in a particular cluster to meet the performance expectations.

5. Active community support

A lot of young minds are focused on expanding the possibilities around Cassandra. They are highly active and provide assistance in case of issues around managing or configuring complex database setups using Cassandra. The monitoring and troubleshooting systems around the software make it a truly high-performance open source NoSQL database system.

Thus, it is clear that Cassandra offers a host of benefits that can add tremendous business value to your tech offerings. It is time that you explore Apache Cassandra for your enterprise database management needs.

How Artificial Intelligence will reshape IoT

How AI will shape the future of Internet of Things (IoT)

The Internet of Things (IoT) has been the topic of discussion for the past few years. It seems as though everyday the IT universe is finding new applications for IoT and its mainstream plausibility is becoming more. While considered a brand new vertical with endless possibilities IoT is just an extension of Artificial Intelligence. The very idea of IoT spawned from the prospects that AI has shown in the past. The idea of devices being connected with each other and communicating is something that is truly a remarkable point in human civilization. When we take a closer look, it sheds light on the extent to which AI has grown and the development it has brought about in other verticals.

While the application of AI in other verticals such as robotics, automobile, marketing etc. create reason for argument due to the various threats they pose, IoT is a vertical that at the moment poses no such threat, unless they start transforming into little killer robots and tearing your house apart.

Artificial Intelligence and Internet of Things

Device Development and AI

Today we have more machines around us than we have human beings. If we introspect it might seem that we are spending more time with our smartphones than we are with other human beings. While a frightening realization, it is the future that we have been building since we first sprouted on this earth. At the brink of achieving that reality, we are now at the stage where we are exploring choices and trying to make the right steps towards them. The devices that we are coming up with reflect these steps and that is where the concept of AI raises some interesting questions for IoT.

While AI is primarily used as the cornerstone of devices, in IoT it plays several roles. There is much there that could influence how the IoT would react with our world. Further along the way it would boil down to the popular paradox of the chicken and the egg. Which technology would shape our future- AI or IoT?

It is easy to argue that advances in both these technologies would be of equal consequence. However, that is not the case. The very correlation between these two verticals is just as defined as how contrastingly they could influence each other. Machine learning is a key aspect of the progress that IoT is making. An IoT network that would consist of devices with sensors, video surveillance tools etc. will be capable of monitoring the functioning of the other devices. For software related issues certain devices will be equipped with troubleshooting tools both for themselves as well as other devices. Data is the instigating factor that could influence all these technologies and it is data that will continue to govern them in the future. The expectations would again fall upon AI to make the best out of the data.

 

IoT in Data Analytics

The idea of developing actionable insights is something that in recent years has provided a huge update for the use of AI and IoT services. As these technologies function using data, the uses become well defined and the margin of error depends only on the validity of the data. This creates avenue for wearable ‘smart’ devices to actually function in a sentient manner. Devices such as the heart rate monitor watches, various goggles allow provide vital data that could be relayed to your doctor, your banker, even your barber, who could avail the analyzed output that they could use to customise the service they provide.

Deep Learning

Deep learning is a breakthrough in IoT. This technology facilitates devices to go beyond the prosaic machine learning algorithm. Deep learning draws from a plethora of sources to arrive at a solution on any given subject. This comprehensive approach to producing solutions could become a key driving force for IoT and how the various devices around us function under it.

Conclusion

The many exabytes of data that is being produced now allow for further proliferation on the IoT front. Going ahead, it is AI’s data analytics capabilities that could facilitate this growth. Both machine learning and deep learning both function on the data that is procured through AI data analytics.

With the AI data analytics process being non-stop, big data and other verticals are proving to be vital resources for IoT. Many industry experts believe, actionable insights will be the key to the future. The possibilities with actionable insights are endless and investments in AI have been made to speed up and increase the productivity.   

How machine learning helps you find the music you want!

Machine Learning enhances User Experience for Music

When creativity meets technology, you get incredible outcomes. And that is what the music streaming industry is investing in these days to improve user experience amidst brutal competition. They push new boundaries with technology and diversify the music genre so that everyone can appreciate it.

How machine learning helps you find personalized music

The era of personalized music with machine learning

In the latest news, the music discovery process is getting personalized results with revolutionary machine learning. Nowadays, almost every big name of the industry is leveraging AI to create better and more personalized music lists.

So, you should not get surprised if the suggested music from Spotify, Pandora and Apple Music seems exactly what you want to hear. All these music-streaming providers implement complex algorithms to pick subtle cues and create personalized music list for you.

  • Pandora combines the same technology with data analytics to make suggested playlists for listeners. The algorithms used by Pandora evaluate the songs or artists selected by a user. With that, it creates a playlist that has similar attributes, matching the personal preferences of that user.

 

  • Spotify is probably the most enthusiastic player when it comes to using algorithm technology in music streaming. The company uses a collaborative filtering approach. The algorithms collect music streaming data from multiple users and compares it together. This comparison is conducted with Echo Nest, which is considered best in this technology for music search. Apart from collaborative filtering, Spotify also includes NLP and audio models in its method of providing personalized music.

How Machine learning is evolving music streaming personalization

As mentioned earlier, music-streaming companies are using a variety of AI technologies to make song discovery advanced and personalized.

Here are three major technologies revolutionizing the music-streaming industry.

1. NLP or Natural Language Processing

NLP enables algorithms to understand human language. APIs are used for sentiment analysis, which harnesses the meaning behind spoken and written words. The model of NLP allows music streaming providers to collect data from a variety of resources all over the internet. Algorithms collect data from articles, news, blogs and other resources available on the internet. Using the written text regarding a music, the machines understand the characteristics and provide them with the right playlists.

2. Collaborative filtering

Collaborative filtering is a comparative study of the users’ music listening behavior. The technology helps in understanding the popularity and characteristics of songs. Algorithms collect data from a wide range of users. These datasets include information regarding stream counts, saved tracks, page visits and many others.

By incorporating all kinds of streaming data together, algorithms create a personalized list of tracks for the listeners.

3. AI audio models

Companies like Spotify understand that NLP and collaborative filtering cannot offer justice for new songs. That is why they use another form of AI-Integrated audio model. This technology works just like the face recognition technology. However, the algorithms inspect the audio models instead of pixels. With raw audio evaluation, companies provide new songs to the users in their playlist.

Thus, it would not be wrong to say that machine learning has found a strong place in the large ecosystem of music discovery. With proven phenomenal outcomes, the justification of marrying AI with music does make total business sense!

How IoT Applications can be used for Agriculture?

IoT in Agriculture

IoT has shown tremendous potential in various gadgets such as wireless speakers, electric appliances, light sources, etc. It has also been used in practical and innovative applications in industries (termed as Industrial Internet of Things or IIoT). Sectors such as agriculture is seen as one of the biggest beneficiaries of IIoT and the various advantages it offers to streamline the farming and cattle-rearing operations.

How IoT Applications can be used for Agriculture

Why the need of IIoT in agriculture?

With the help of Industrial Internet of Things, the agriculture industry is evolving to enable growers and farmers face various hurdles on the field efficiently. Before the implementation of this technology, the sector was facing low rewards, heavy workload on labor, and high risks. Moreover, farmers used to face unexpected risks, economic recessions, and sudden changes in the environment as well. All this greatly affected the overall growth of crops in fields, until now.

How IoT can evolve farming?

IoT has a lot of scope in agriculture. So, let us have a look at some of the ways it can improve this sector.

1) Controlling climatic conditions in greenhouses

Greenhouses require subtle conditions so that the growing plants can stay healthy. Previously, this process was quite labor-intensive, but with the help of IoT-based equipment, it has greatly helped in reducing the burden on growers.

The process involves using sensors that monitor attributes like soil moisture, intensity of light, humidity, temperature, etc. These sensors connect to appliances that automate processes such as air or water control. Some sensors are even smart enough to deduce signs of pest infestations.

2) Safety of crops in logistics

IoT technology has also evolved the supply chain management of agricultural products, retail and logistics. Farmed food that is shipped to various places is tagged using Radio Frequency Identification (RFID) tags that help in easy tracing and tracking.

This increases consumer confidence and transparency levels about the source and origin of the food product they would be consuming. Some IoT gadgets used for monitoring the crops are so advanced that they provide real-time data for packaging, transport, and storage of farm foods.

3) Monitoring crops

A lot of IoT-based machines and robots are coming up to help in experimental farming. A few of these are designed for monitoring crops in a field. With synchronizing capabilities, these machines are able to record data like yield maps for crops or link information related to crop prices.

Such robots are said to be so capable of their sensors and features that they are able to keep track of every single crop stalk in fields.

4) Livestock farming efficiency

Not just plants, but farm animals can also be monitored using this technology. Internet of Things can monitor problems like infection threats among chicken, cattle, etc. and inform farmers about it before it gets too late. Even food intake habits can be monitored for farm animals that can help in deciding the right food for them.

No doubt, the world of IoT has so much to offer farmers in the world of agriculture. The ROI that this investment will bring is bound to outweigh the costs associated with it. Hence we can expect to see rapid adoption of IoT technology in the agribusiness.

 

Walk-in Interview for UI Developers

GoodWorkLabs is hiring and we are conducting a massive interview drive for UI Developers at  Apttus Software Private Limited – Bangalore.

This is your chance to boost your career and work on some amazing projects.

UI Developers interview

Walk-in details: Saturday 24th March | Time: 9 am to 2 pm

Job Description: UI Developer

1. Should have a minimum of 3 years of work experience

2. Should be proficient with Angular JS, Javascript, HTML5, CSS3

3. Have an enterprising attitude and be a goal-getter.

 

Walk-in Interview details:

Date: 24th March 2018 | Saturday

Time: 9 am to 2 pm

Address: Apttus Bangalore Office, 5th Floor, Salarpuria Touchstone Sarjapur, Varthur Hobli, Outer Ring Rd, Marathahalli, Bengaluru, Karnataka 560103

Contact Person: Malar

Contact number: 9739691607

 

If you are looking for that ONE chance to give your career a boost, then drop by for an interview and walk out as a Technology Superstar!

Why Product Thinking is important in UX Design

How to use Product Thinking in UX Design

The original premise behind user experience which is often understated is the fact that lies in its namesake itself- to make the user’s experience with any product better. Product designing is an intricate and complicated process where the designer could get lost in a coded web and in all the ruckus it is easy to ignore the user’s needs. Designers can hardly be blamed for doing so because every product has a fundamental purpose it tries to serve which defines its existence.

The features of a product are hardly of any consequence if they don’t satisfy individualized needs and goals. This redundancy factor is what prompts a need for more comprehensive strategies like product thinking.

Product thinking in UX Design

What is Product Thinking?

To put it in simple terms product thinking is a strategy where the product is the sum of all the users’ expectations. Here the users are the instigating factor and the product becomes the end result. The common pattern followed with product thinking is as follows:

Start with the user

  • What is the problem your product would seek to address?
  • What is the target audience?

The Job at Hand

  • The idea behind it?
  • How would you go about executing it?

Your Expectations

  • The goals you seek to achieve.
  • The resultant features that come out of the aforementioned efforts that would go into your product.

 

Product before Features

A common flaw when it comes to designing is the amount of emphasis that designers put into features. While features are important, for most designers building a product means creating a preset of features that will eventually define it. This is where they lose touch with what the user actually needs. With product thinking, the idea is to visualize the product during inception as it will be presented to the users. The features are then added in to complement that idea and build the ideal product.

 

Defining the Product and its Purpose

The level at which user experience is today, understanding the user is not that much of a task. With such valuable resources at their disposal designers get a clear picture of their target audience, their issues, the vision behind the product and its end goals. However, this part is easier said than done because when it comes to users their problems are often latent and it is up to UX professionals to uncover them.

Once designers comprehend the purpose why people would purchase their product in the real world, they can create a rough idea of what the product would be like, what it would mean to them and what ends it would serve. Once this core aspect of the product is laid down, the features will automatically fall in place and the designers can tweak them in any way that would ultimately enhance the user experience.

 

Problem vs Solution

For a designer, there are many ways to go about solving problems with a product but understanding the heart of that problem is what sets the bar for innovation. Many see problems as a prosaic concept where consequence translates to causation. But, in the real world problems with a product can be anything. In some cases, things that are seemingly negligible could be causing people to walk away from your product.

The complication here is that people themselves seldom realize this fact. The users know that they don’t like the product, but they can’t explain why. So designers have to delve deep into the psyche of the users to understand the problem and fix the product and its features.

 

Conclusion

Product thinking in its essence is the combined effort of everyone involved in a project. It is as abstract as a concept as to implementing it. Yet without it, a design is nothing more than just a UI. The layers of research from both the product management and designing point of view leave little room for doubt when it comes to users.

 

Data Driven Advertising with AI and Machine Learning

How AI is changing the Advertising and Media industry

Over the past decade or so advertising has changed drastically. From the humble copywriter/editor complement, advertising today has turned into a multidimensional effort with professionals from multiple verticals pitching in to achieve the end result. This, of course, is no surprise considering the deep impact that IT has had on almost anything and everything. If a copy editor were to tell you 15 years ago that his computer will be taking care of your advertisement and its standing, you would have thrashed him with his keyboard and taken him to a mental asylum. Yet here we are at the pinnacle of IT (as far as we know) and computers are planning ad placement, bidding for keywords and updating you the status of their efforts. So the question we need to ask ourselves is how far can this be leveraged.

The use of AI and Machine learning for such processes is nothing new. As a matter of fact, the current usage of AI in advertising is still relatively primitive. But the inroads we are making through the use of this technology is substantial. However, for AI and machine learning to make any sort of assessment the most important thing is data and it needs lots of it.

AI and Machine Learning in Advertising

What is Data Driven Advertising? 

Anything you do on the internet required the use of data and while you do it generates data as well. From a business perspective, one of the biggest reasons why organizations use the internet is for advertising. Advertising is a multi-billion dollar industry with many dimensions within. Among them internet today is the most prominent and offers the most comprehensive results. So what kind of data is it that floats around the internet to help out with advertising. Well, the answer is pretty much everything, from search histories to personal information, social media updates to data pertaining to behavioral attributes, the internet is a repository for all these. Big data as we all have come to know it is what drives this process. While in the past data-driven advertising was largely based on manual analytics, the vertical today relies on automated technology.

 

The AI and Machine Learning Influence on Advertising     

While still in their inception stage, both machine learning and AI are being used more than we might have anticipated.

 

  • Search History: Most of the data that is available today on the internet comes from search history. For advertisers, tools like Google AdWords offer keyword suggestions that tend to draw in more viewers based on their activity. This largely automated service provides an edge over competitors to place your ads with the right keywords. Be it product, service or information, anything you search for on the internet gets registered irrespective of the search engine. This information is then transferred to the highest bidder like in the case of Google as part of the google analytics tool. So advertisers who are registered with the tool gain access to your location, the products you were searching for, your brand preference if you have purchased anything and so on.

 

 

  • Voice Recognition: Online shopping’s next frontier-voice recognition devices like Amazon’s Alexa and Google Home are currently taking the entire e-commerce sector by storm. The ability of these devices to relay your requests as well as make suggestions based on your activity is truly something that will be influencing e-commerce in the years to come.

 

 

  • Social Media Bots: While being the cause of much controversy recently, the use of bots in social media has made the process of gathering information lightning fast. Social media is the source for a plethora of sensitive information and since inception has been exploited by advertisers and marketing companies to plug their products.
  • AI Content Creation: The use of AI for content creation particularly for social media and BuzzFeed, have truly revolutionized advertising and marketing. Several types of content on multiple platforms are being written by AI today. Still, in a primitive stage, this is a technology that will surely pick up in the years to come and who knows maybe be even replace human writers. It is predicted that by 2043 we might have the first number one best-seller authored by AI.

 

 

Conclusion

While the current state of AI does leave much to be desired for advertisers, we are not that far from perfecting it for that purpose. There are plenty of prospects to be had on the advertisements themselves as AI and machine learning develops. Machine learning, in particular, could be leveraged to design ads without any human involvement. Currently, technologies such as deep learning are being used in the imaging process in games and movies, which point to good prospects on that front.

There are many more technologies that are still in the prototype stage being tested under various scenarios to eventually integrate into the mainstream processes within advertising. So let us wait and watch as this bit of technology evolves and its story unfolds.  

Why User Persona is important for UX Design

Getting to Know User Persona for UX Design 

A vital part of User Experience design or pretty much any business-based technology, user persona can be defined in simple terms as a virtual representation of your typical customer. Derived from user research and web analytics a persona serves many ends for UX designers. A persona usually includes qualitative and quantitative information of a user’s personal attributes such as their behavior patterns, likes and dislikes, needs, ambitions and so on. A persona can also be defined as the collective representation of a particular demographic using a typical user which the designer can use to relate. This also provides you with a valuable tool while trying to pitch ideas to stakeholders, designers, and anyone who is involved in your project.

user research for designing

How do you Create One?

There are a multitude of ways for creating a persona. But the most commonly followed ones are those from user experience research methods. Through interviews, casual interaction and such, one can ascertain enough information about the user and build a profile of their general attitude, character, and behavior. While direct research methods are quite commonly used, web analytics also plays a large contributing factor. Social media websites and applications and search engines too are valuable sources of information to create such a profile.

 

A persona usually consists of the following aspects:

1) Personal Information

Vital information such as name, age, marital status, number of kid etc. are a staple in all personas. Usually accompanied by a photograph, this statistical information helps build a clear demographic profile of the user.

2) Environmental Data

Information pertaining to the users’ social life, work life and the technology they use and so on. This helps build a profile of the surroundings they live around and the kind of company they keep and their work ethics and profile as well.

3) Psychological Data

Information regarding their personal interests such as food, movies etc. along with their attitude, strengths, weaknesses and pain points can all be found in the persona as well. These give the profile a human touch beyond the rather mechanical statistics.

4) Personal Ambitions

One of the more inquisitive features of a persona is the information regarding goals and ambitions. While other information is quite vital, goals and ambitions provide an idea of exactly what the user needs, and how your product could influence if not help achieve it.

5) Scenario

This is the final aspect of a persona and is created based on all the aforementioned aspects. The users are put in a typical real-life scenario and an analysis is made on how they might interact with your product.

 

What are the different type of user personas

While most user personas are created from actual users, to serve your marketing and designing agendas, some organizations build personas without any actual information. These are called proto-personas. While proto-personas like regular personas are used as a means of actionable insights, their information is usually the result of guesswork and experience using which people create their own image of their typical customer. The reasons for this usually are either time or budget constraints or both.    

 

So what do you do with user personas?   

The most fundamental purpose of a persona like mentioned above is to serve as a communication tool while pitching ideas with your team and anyone else who is involved in your project. For a designer, a persona serves as a guideline and inspiration to design the product for a particular demographic. All the information pertaining to the users’ emotional and psychological state along with their interests and goals allow the designer to clearly conceptualize what the product should be doing and how it will be serving them. The organization as a whole can also gain valuable insights and stay in tune with their customers’ lives.   

 

Conclusion

Despite the many research tools available today, user persona serves as a reliable tool for both designers and marketers to understand the product and their users. The plethora of small information available in personas generally helps out more than elaborate details on a particular aspect of the user. Additionally, when you have access to multiple personas, the common points between them serve as a deciding factor.

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