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Last week I wrote about Google’s new Chatbot Meena, which resulted in a lot of feedback from my readers.

Thank you for that. I appreciate this reader engagement a lot.

Someone asked me if Meena is also a Virtual Assistant aka VA?

I had to think about this. a VA is, in my honest opinion, software that helps you with organising and completing your work, like an online calendar, a password manager, a dictaphone, a video conferencing tool or a to do list.

What about Chatbots? Do they also belong to the VA family? Or are they completely different? To answer this question we first have to know what both software agents are and how they are used in our daily life.

What is a Chatbot?

These are software agents capable of simulating a conversation (a chat) with a user in natural language through messaging applications, websites, mobile apps or through the telephone.

In recent years they have been widely adopted by companies to interact online with their customers: lead generation, sales and customer support.

Chatbots are programmed to respond with a certain set of answers and statements.

What is a Virtual Assistant?

As already said, a VA is a software agent that assists you in performing your daily activities like setting clock alarms, scheduling appointments, making (video) calls etc. Virtual Assistants are in that sense similar to personal human assistants. They help you get things done.

What are the similarities between Chatbots and Virtual Assistants?

They are both user interfaces designed to interact with a user, a human. The interaction is based upon pre-defined commands or questions.

They are now an important part of our (online) life helping us with answering daily questions.

Here, Natural Language Processing (NLP), an important part of machine learning, plays a key role.

NLP can help to understand how a text (Chatbot) or voice (VA) – a natural language – has to be interpreted and processed, so the software agent can react to it effectively.

This is not only limited to words, also conversations can be understood with the help of NLP.

NLP enables Chatbots and VA’s to be trained with different conversations so they can react effectively when the user interacts with them in a similar conversation already trained.

Vital for an effective conversation is knowing the intent of the user and the context of the conversation.

What are the Differences between Chatbots and Virtual Assistants?

The difference is based mainly on usability. Contrary to Virtual Assistants, Chatbots are only text-based and work on a single-turn exchange. In other words, it reacts to a question by the user, interprets it and gives a single pre-defined answer. If the question the user asked does not match their learned set of knowledge then they fail to answer.

Next to that, Chatbots are software agents used in business, Virtual Assistants are more used on a personal level.

Chatbots are more used in business because they can automate simple tasks for all clients. A VA on the other hand, is more uniquely associated with an individual user. Their range of tasks is also bigger, they have a wider scope than Chatbots, mainly due to the simpler algorithms the latter uses.

And don’t forget, the VA is a voice assistant, capable of interpreting also voice, contrary to the text-based work a Chatbot can do.

Virtual Assistants can improve over time, because with every user-interaction they learn more about their user. That is why these software agents can manage your daily activities.

Both software agents use Natural Language Processing, but the Virtual Assistants are more sophisticated in this, because they, as already said, use more sophisticated algorithms and improve over time due to their machine learning capabilities. This enables them to give contextualized answers back to the user. This does not mean there are no Chatbots using machine learning or artificial intelligence. More and more software developers are using these to improve their bots, but this takes some time. A lot of software companies say their bots have machine learning capabilities, but these companies base this on using IF-THEN-ELSE in their code. My dear readers, that is not machine learning, that is computer programming.

Is Siri a Chatbot?

Now we have covered the similarities and differences between the two software agents, let’s consider real life examples.

Siri on your Apple mobile device does not belong to the Chatbots, but it is a Virtual Assistant, capable of executing tasks or delivering services by means of speech recognition. It is even capable of opening apps (like navigation or calendar) and retrieve the information necessary for answering your question. It is your personal Virtual Assistant, usable in business and at home.

What is Meena then?

Now we have seen Siri is a Virtual Assistant, what about Meena?

Meena is an example of an open-domain Chatbot — designed to converse on any topic that can function as a “friend,” advisor etc. This is different than the closed-domain Chatbots which are programmed for specific Q&A.

But unlike Siri, it is a textbot, and not a Voice Assistant, so it is, in my honest opinion, still a Chatbot and not a Virtual Assistant. Although if you combine it with Google Assistent and implement it correctly, it will become an awesome Virtual Assistant, capable of many tasks and services.

Wrap-up

We discussed the similarities and differences between Chatbots and Virtual Assistants.

Chatbots are business-driven text-based online software agents and work on a single-turn exchange, used mainly for non -complex customer interaction like lead generation and FAQs.

NLP and machine learning is used to widen their scope, but this is still in research and development.

Virtual Assistants like Siri on the other hand are used on a personal level, have a wider usability scope.

More importantly, they can improve over time, because with every user-interaction they learn more about their user through machine learning.

Although more and more of the Chatbots are also benefiting from machine learning these days.

One of these is Meena, the Google Chatbot, which is still in research and development, but has high expectations, because it is designed to converse on any topic that can function as a “friend,” advisor etc. This is not the case with the current Chatbots.

I am curious what Meena and the next generation Chatbots and Virtual Assistants will bring.

Did you like this article?

And do you also want to share such news on your own company’s blog or knowledge base, but you do not know how?

Contact me NOW at TestingSaaS and let’s setup your company’s knowledge base together.

Chatbots and digital assistants are very trendy these days and more companies use a chatbot or a digital assistant in their daily communication with clients or co-workers.

But honestly, when you use Siri or Alexa, was it a satisfying conversation? Was it really a conversation?

Google claims it has now build a new chatbot which can talk to you about anything, just like a human.

Meet Meena.

What is Meena?

Meena is an end-to-end, neural conversational model that learns to respond sensibly to a given conversational context. A neural network models itself after the human brain by creating an artificial neural network that via an algorithm allows the computer to learn by incorporating new data. It’s a form of machine learning. Here it’s conversational, because the computer gets the data by having a ‘conversation’ with the user.

Simply said, Meena has learned to respond correctly when needed in a given situation.

How is Meena, the chatbot, built?

Meena is a conversational neural network with 2.6 billion parameters.

Google’s development team trained Meena with 40 billion words, that’s 341 GB of textual data.

How did they do that? By using the Seq2seq model.

A variation of Google’s Transformer, which is a neural network that compares words in a paragraph to each other to understand the relationship between them.

Meena is build of a single evolved transformer encoder block and 13 evolved transformer decoder blocks. Encoder blocks help Meena to understand the context of the conversation, decoders help Meena to form a correct response.

A little history of Google and chatbots

Meena is not Google’s first experiment with chatbots.

In 2015, Google released a paper on a model that helped with tech support. The company also developed a lot of language models to better understand the context of a conversation.

So you see, Google’s R&D was quite busy with chatbots in the last 5 years

Google’s competition regarding chatbots

How is Google’s competition dealing with chatbots?

Amazon had its Alexa Prize challenge in 2019 where it challenged teams with building a bot that can sustain a conversation for at least 20 minutes.

Another tech giant, Microsoft, acquired in 2018 conversational AI startup Semantic Machines to improve Cortana.

Is Meena the chatbot open source?

Awesome news, Google’s new chatbot Meena.

I want to code with it too, is it open source?

Unfortunately not. But maybe in the future.

Did you like my blogpost on Meena, Google’s new chatbot.

And do you also want to share such news on your own company’s blog, but do not know how?

Contact me NOW at TestingSaaS and let’s setup your company’s blog together.

Burst the bubble in IT marketing, that’s what TestingSaaS is going to do in 2020.

Why?

Every time a new technology in IT appears on the surface, new buzzwords are introduced and heavily used by the IT marketing pro’s.

This is not something new, it exists as long as IT itself. Buzzwords are used to market the product and ‘show’ the public something ‘new’ and ‘state of the art’ has been built for them to help them with their IT challenges.

In 2007 I started the TestingSaaS blog to burst this ‘buzzword-bubble‘ and show the readers these buzzwords are just the same sh*t with other flies on it. Since that time I have written about new IT-‘innovations’ like digital identity, test automation, data science (remember big data), DevOps, information security, chatbots, augmented reality etc.

Great, so I am doing this for about 13 years now, why do I want to emphasise this in 2020?

Why creating clarity in IT marketing in 2020?

It is the beginning of 2020 and a lot of articles are written about the IT-trends the coming year.

I was triggered by an article in the Dutch management journal MT: Next Generation Leadership about 9 AI-trends in 2020.

I know the writer, Remy Gieling, and enjoy his articles.

A lot of buzzwords are present in this article, but Remy explains them well or links to other articles where they are explained.

This is a great example of how I would like to read an article about IT.

Unfortunately a lot of articles are still out there just bloating buzzwords without explaining them, leaving the readers confused. I will not share the examples, you know they exist.

Help the IT company sell by explaining the buzzwords

So, what will be my main focus in 2020:

Help the IT company sell by explaining the buzzwords to their customers who are overwhelmed and confused by these buzzwords

This can be done by an in-depth article, tutorial, knowledge base, FAQ, infographic etc.

Mind you, a lot of IT buzzwords are now combined in the marketing of single products and if you, as an IT company, do no know what they mean and how their interaction is, you do not know the product. If you do not know the product, how can you sell it?

Let me help you burst this buzzwords-bubble and be transparant again and show the customer you know the product you sell.

Contact me now and let’s bring clarity for your customer together, resulting in a great customer experience and a long lasting relationship!

Do you remember the macOS antiforensics course I designed with eForensics Magazine last year?

Well, it is now running for a year and it is still going strong.

Many infosec enthusiasts followed the course and have given me great feedback via eForensics Magazine and LinkedIn.

Thank you all!

Can I still apply for the MacOS antiforensics Course?

Don’t worry, you can still apply for the course and follow it in your own pace.

Have a look, as already said, it is a self-paced practical, easy-to-read online course with lots of visuals and videos. And it’s fun!

A new infosec course by Cordny?

Who knows?

Maybe another macOS course, or antiforensics tooling?

I keep you posted!

Have a great and fun 2020!!!

Test automation is an important part of my work at TestingSaaS.

Ever since I started software testing I was intrigued by automating it and finding a great tool doing this.

This journey took some time, but I found a great partner delivering me the tools to do some awesome test automation: Sauce Labs

What is Sauce Labs?

Sauce Labs ensures the world’s leading apps and websites work flawlessly on every browser, OS and device. Its award-winning Continuous Testing Cloud provides development and quality teams with instant access to the test coverage, scalability, and analytics they need to deliver a flawless digital experience. Founded by the original creator of Selenium (woohoo!), Sauce Labs helps companies accelerate software development cycles, improve application quality, and deploy with confidence across hundreds of browser / OS platforms, including Windows, Linux, iOS, Android & Mac OS X. Optimized for Continuous integration (CI), Continuous delivery (CD), and DevOps, the Sauce Labs platform is built to handle the most secure data from its customers, who range from Fortune 500 companies to small businesses worldwide. To date, nearly 2 billion tests have run on the Sauce Labs cloud.

TestingSaaS and the Sauce labs test automation software

As a software tester I use a lot of Selenium for automated web testing. But I also use Sauce Labs to test other applications:

• Real-device testing
• Chatbot testing (together with Botium)
• AIOps
• Mobile app testing
• Contract testing with PostMan

And, as you can see, because I like to write about software, I also take my readers along my journey. After all, my mission is to ‘Create Content by Testing‘.

Thank you Fixate and Sauce Labs for joining me on this journey.

TestingSaaS and test automation in 2020

Test automation is a fast evolving field.

Artificial Intelligence is getting more and more important in today’s information technology. And what to think of all the new mobile devices?

How can we use test automation so tests can be done in time to market and ensure the quality of the systems under test?

Enough to test (automate) and write about in 2020.

TestingSaaS will continue its ‘Create Content through Testing‘-journey with the help of Fixate, Sauce Labs and its partners.

So, do you want to join my journey?

As a content marketeer I work a lot with Content Management Systems, also known as a CMS.

A lot of these CMS are online available and therefore can be tested very easily. Take for example WordPress. If I find a bug I can share it with the WordPress community very quickly and actions can be taken.

Did you know TestingSaaS partnered with SpotOn to test and market their CMS for Augmented and Virtual Reality?

In my last blog I already discussed this when reporting about Adobe Aero.

Content delivered via AR/VR is going to be booming in 2020.

My mission in 2020 is to test the AR/VR Content Management Systems and monitor their quality.

And make first class content of course for my clients.

Do you want to join me in my mission?

Last week, November 4, 2019, Adobe launched its AR Authoring app, Adobe Aero. Awesome, what is it and what can you do with it?

AR is getting more noticed by the big tech companies. One of these is Adobe.

Adobe first demoed its Aero AR authoring app last year at the Adobe Max event, and recently, November 4th, it launched it to the public.

What is Adobe Aero?

Adobe Aero is Adobe’s answer to designing AR without coding.

Aero is the first tool that allows designers to build and share immersive experiences in AR—without any coding skills.

If it’s the first AR no coding-tool, I wonder. But that’s another blog.

I already mentioned it is freely available to the public.  As long as you have iOS, a free mobile iOS app for phones and tablets is available.

But before going to public a private betatesting program was set up. During this beta, Adobe received participation from thousands of users within the creative community. Adobe also launched the Adobe AR Residency program to collaborate with the creative community for building the best tool available.

So, what features were developed during this betatest?

• Intuitive authoring – No coding necessary. all steps done via easy-to-follow instructions
• Animation – Objects can be placed by using your hands and device. This combined with triggers gives a great animation.
• Assets at your fingertips – Hundreds of free starter assets available right in the app, including the option to import a broad set of 2D/3D file format (vector graphics, Adobe Photoshop files, OBJ, FBX, Collada, glTF, etc.)
• Easy publishing and sharing – sharing your AR product directly on social media or sending the experience to others via the Aero app.

What can you do with it?

Developing AR without coding brings Media to the next level. Media artists are no longer dependent on programmers to do the coding for them. Now they have a platform for this.

Digital is no longer confined to a single screen – it’s permeating throughout physical spaces and the real world.

Artists can also add interactive experiences by drawing the path of motion for an object to follow.

It’s the next step for Adobe. From Photoshop for graphic design to Aero for the Augmented Reality space.

The era of Augmented Reality CMS has begun. There are more examples of AR CMS on the market. Remember, this is just the beginning.

TestingSaaS will monitor this evolution and keep you updated.

Smart speakers are trendy in 2019. Especially smart speakers with virtual voice assistants are booming.

But what challenges do the manufacturers of these smart speakers face with user adoption of its virtual voice assistants?

This blog will discuss 3 of these challenges.

Remember the Eighties Hit series The Knight Rider where The Hoff could communicate with his car by voice?

This is not science fiction anymore.

Smart speakers with built-in virtual voice assistents are booming and it is now possible to instruct your smart speaker to enable specific activities like playing your Spotify list, ordering food or groceries or read out loud the daily news and weather. How convenient is that?

However, some challenges have to be addressed to enable user adoption for voice assistance.

Installation and Customer Experience of a Voice Assistant

Building a smart speaker with voice assistance is not easy in this crowded consumer market.

is it not already there? Does the consumer really need it?

Do you have to be a whizz kid to install it and afterwards? How many times do I really use it?

Just a few of the questions a manufacturer has to ask himself before putting the product on the market.

If he can’t answer these questions there is a real big chance the consumer won’t like the product and it can become a financial disaster.

Voice Assistants and Brand independence

Great, you have a couple of smart devices in your home and, for convenience, you expect them to communicate together.

Not every person wants all devices from a specific brand, say Samsung, but also from Google Home or Amazon Alexa.

If you want a great user adoption you have to ensure your device is compatible, also with voice assistance.

Voice Assistants and User Privacy

Data and user privacy is an important field a voice assistant manufacturer has to deal with.

Online clicking is different than voice data, Why?

First, voice is biometric and can be used to identify a person, like a finger print or a retina scan. Next to this, it can also identify a person’s mood or even his mental state.

This is quite different than to know which button this person clicked.

Also the environment the smart speaker is in, should be taken in account. These are personal, intimate surroundings, your own home. Remember the recent disturbing news of smart devices listening unnoticed.

The user needs to know he is safe and his privacy is not violated, otherwise user adoption is certainly a bridge too far.

As you know, I am testing WeChat at the moment. One of its features I am curious about is the WeChat shake functionality.

What value can this give to the user and is there a business case?

I will discuss this in this article.

What is WeChat shake?

When this feature is selected, just shake your phone. You will be connected to someone else in the world who is shaking his/her phone at the same time. Then you can choose to ignore or respond to this user.

The value

WeChat shake can be a great way to meet new people while using your phone. This can help user feel less lonely.

But is there also a commercial use case?

duplicating the Chinese digital ecosystem in Europe

In China WeChat shake is very popular and not only because it helps against loneliness.

There is also a commercial benefit. In shops where the WeChat shake-logo is present, customers with the WeChat app can shake their phone and then get the offers/coupons from this store on their phone. This is already present and used a lot in far away China, but also will be implemented in Europe.

It seems this feature of WeChat is based on iBeacon-solution, but I am not sure yet, because recent online documentation is scarce on this part. So this will be a future research project.

Let’s see how this implementation of WeChat shake will continue in Europe and if the Chinese tourists will use it.

To be continued ….

A TestingSaaS guestblog on A/B testing by Ilan Nass, Chief Strategist Taktical Digital

Facebook is a valuable marketing tool. With approximately 2.38 billion active monthly users, the platform gives you access to many potential leads.

But if you think Facebook Ads are something you can set and forget, think again. To get results you desire for a sane amount of ad spend, you need to study your results and optimize accordingly. There are a lot of metrics to track when it comes to creating ad campaigns that slice through the noise. Once you’ve decided on a smart metric, it’s time to design an A/B test. This simply involves testing multiple versions of an ad or campaign to determine which is most effective. The following points will help you better understand how to go about this process on Facebook.

What Not to Do

Before exploring how to properly A/B test, it’s important to brush up on mistakes you should avoid. They include:

• Testing too much content at the same time: If you test too many ads or pieces of content at once, it will be difficult for you to confidently identify which content yielded results, and which failed.
• Testing minor changes: When A/B testing ads, they should be different enough that you can glean genuine insights from their performance. Simply changing one line in an ad isn’t enough to help you understand what does and doesn’t work.
• Not providing equal opportunities: You need to design your test so that both ads involved have the same opportunity for success. For instance, if one had a higher budget or was targeting a stronger audience, it might outperform the other, despite not truly being any better.

Instead, follow these tips to A/B test effectively:

Only Test a Single Element

Again, it’s important that your ads be noticeably different when A/B testing on Facebook. That doesn’t mean you should make every element different. You’re better off changing one element when generating multiple versions of the same ad. Change the ad copy, or the image, or the audience, etc. While the changes need to be substantial enough to make a difference, they do need to be restricted to a single element.

Don’t Test Merely Two Ads

The name “A/B testing” implies testing only two versions of a given ad at a time. In some instances, this may be appropriate, but you can often get more valuable insights if you test three to five versions of an ad. When doing so, you may want to make the ads significantly different.

For example, maybe you’re running an ad promoting a product. One ad may feature an image in which the product is foregrounded, with accompanying text explaining its features and benefits. Another ad may include minimal text, with an image that shows the product in action. Yet another might include substantial text in the image, with the product itself taking up less space. This gives you three ads that are different enough from each other to offer valuable information.

Don’t Draw Conclusions Right Away

You need to give your ads a sufficient amount of time to reach users before drawing any conclusions. For example, if you ran an ad asking people to sign up for your email newsletter, with one slightly outperforming the other after a week has elapsed, you might assume that ad is stronger. Although this may be true, it’s smarter to wait two or three weeks to start analyzing data. The more data you have, the more confident you can be in your insights.
Keep in mind that A/B testing does require you to invest some time and money. However, in the long run, the value is worth the investment. Knowing what types of Facebook ads resonate with users is key to optimizing your return-on-investment.

Do you also want to guestblog on TestingSaaS?

Contact me now and let’s start blogging.