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Chatbots & Cloud CRM Are Replacing the Call Center Using AI Algorithms





Telemarketing, telecommunications and service call centers traditionally relied on human service operators interacting with customers over the phone. This was for many years just done over the phone until the Web and things such as e-mail took off. Over time, more and more service centers started to outsource employees and customer service to third world countries where cheap labor could be found (there are also companies specialising in such services).

For many years customer service relied on customers calling up a service center and asking for help in fixing their system or an error with the software they are using. A call center representative would try to help the customer over the phone from a single location and if that did not work they would route them to a technician or specialist that could potentially access their system remotely. Customers would spend hours over the phone with the potential of a connectivity loss. Over time, cellphone (now smartphone) usage became widespread, which alleviated this issue somewhat.

However, synchronous chats and online communications really took customer service to a higher and more individualised level. Nowadays the practice is expanding into artificial intelligence or machine learning along with the cloud and other technologies replacing many employees or humans sitting at these call centers.

We have these programs being deployed across websites and messaging services called chatbots that even Facebook uses in its messenger to help customers instead of relying on human operators waiting to take a call at all hours of the day and night: often not a feasible task.

A chatbot is available on demand and can serve as the first line of help for any customer that may be accessing the system from a different geographical one where the company is based out of and in the middle of the night in the company’s location. It can also aid customers with both text and voice as well as through visual cues.

Chatbots are becoming more robust and effective due to them implementing AI or machine learning techniques in helping solve customers’ needs and understanding human language better or the questions people input on their keyboards. Some of you may recall a time when you entered a question or even a search query on Google and got a completely different answer than what you intended to ask about. Chatbots are increasing efficiency in the ability to weed through questions asked in a variety of ways by the end user.

Besides chatbots, there is also contact center software available that allows companies to manage all their communications from a single source (or cloud-based CRM) and respond effectively to customers on demand. An example of this is Vocalcom. Communication is becoming much more digital and Vocalcom also has a enterprise contact center service that is integrated with SalesForce.

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Service center solutions such as these and CRM systems like SalesForce’s allow companies to stay in touch with customers through smart business intelligence and analytics to stay ahead of their customer’s needs. Another good example is that of Ujet. Both of these service center solutions rely on cloud integrations.

Machine learning and chatbots are really driving much of the global communications today amongst help desks or C2B assistance. It isn’t easy to sometimes hire interpreters or communication experts in a wide range of languages if the company is expanding to other geographical regions. A chatbot can do this on demand. It will also start to understand and pick up customer habits in terms of what they are asking for and how to help them effectively as the languages and audiences change.

A smart solution for business today is to have integrated systems in place that include both chatbots and cloud service centers on demand. Ujet has an interesting solution in place that allows for real-time monitoring of agent activity, intelligent call routine logic and the ability for supervisors to problem solve before issues arise, which is smart business intelligence. Personalization is also key as companies are becoming more tightly focused and specialized on particular market segments and customer needs.

“Customers can contact your call center, schedule an agent callback or launch a chat session directly from your website or mobile app,” according to Ujet’s website. “They can use their smartphones to share visuals that help agents better understand their issues. And service is speedier because agents already have key details about them, and their potential needs, right at the start of a call or chat.”

Ujet is referring to traditional chat communications between clients and companies, however a chatbot can also be integrated into such solution for times when company reps are not at work and as a first-step communication mechanism for companies experiencing heavy traffic load from customers.

Companies can implement multiple solutions today with the aid of SaaS and leverage various tools available from the cloud or CRM systems. They can then start integrating machine learning, such as chatbots, and have various ways to service clients through effective communication. Ujet has more information about the potential of such integrations in customer service on their company’s about us page. It summarizes this form of customer support well:

Customers can easily reach contact centers by phone, web and mobile app and use the power of their smartphones to help explain their problems. Agents have the contextual information they need to assist customers quickly, as well as more time to focus on resolving their issues. And supervisors have intuitive tools and real-time intelligence to get the best performance from their teams, increase customer satisfaction scores and help grow the businesses they serve.

In terms of the first layer of communication, I argue this is where the chatbot should come in —- at least for international companies with customers worldwide and those experiencing heavy traffic spikes (where call centers may be slow to respond to clients and even cloud contact centers can experience issues effectively addressing customer complaints on an individual level. Examples of this would be not enough employees for example and too many emails and chat clients.

SalesForce has an interesting video embedded on its site about chatbots and their use of machine learning advancements titled Chatbots: Using AI to Deliver the Future of Customer Service. You can watch it here and learn how chatbots are hot to implement right now and will only improve in the future as machine learning improves and the longer chatbots are active the more effective they become as tools for customer communications and services.

Companies can even utilize their own ideas for chatbots and deploy a chatbot they customized for their needs from scratch on botpress. This is an “on-prem, open-source boy building platform for businesses,” according to its website.

Some of its key highlights include NLU: natural language processing (NLP); analytics; flow editing: the ability to edit conversational flow; multi-channel across various messaging channels; authoring UI: ships with a GUI of its own for non-developers and SDK along with various APIs that are extensible and customizable.

This is just one example of many solutions out there for creating a comprehensive chatbot today with regard to natural language processing capabilities. Oracle has one as well that relies on JavaScript and where the chatbot can be deployed as localhost or off Oracle’s cloud servers. It is called Oracle Intelligent Bots.

I have experience creating chatbot variants from three different providers that I covered in depth for Pluralsight as technical guides. These three chatbot platforms I used to create them include Microsoft Azure, MotionAI (now Hubspot) and IBM Watson.

Natural language is not east to interpret or understand as a question can sometimes be asked a thousand times. It is in this way that machine learning is strongly being pushed. Google search, for instance, relies on machine learning to interpret search queries as do chatbots. Combined with effective CRM tools that leverage the cloud and cloud customer communication solutions is a great way for a company to service customers on a personalized level and identify problems before they arise on a massive scale. Customer service is not just becoming more personalized, but data driven and intelligent.



7 Awe Inspiring AI Techs That Transformed The Digital World




For many people worldwide, artificial intelligence is slowly making its way into their lives without fussing. From our cars, homes, mobile phones, and our workplace, Artificial Intelligence is everywhere.

Apart from our personal lives, Artificial Intelligence has also made its way into various industries such as automotive, e-commerce, healthcare, and entertainment.

To better understand artificial intelligence’s impact on the digital world, we first need to know what it is. To sum up,

“Artificial intelligence reduces human intervention with the help of algorithms and tools that provide recommendations, predictions, and decisions through real-time data.” 

Now that we know what artificial intelligence is, we can move forward and find out how it transforms the digital world. The use of artificial intelligence is every present and visible in our daily lives.

With the help of a machine and deep learning, artificial intelligence has found its way in computer vision systems, image processing, and voice recognition applications, transforming them in a way never seen before. If you want to know how Artificial Intelligence affects the digital world, this article will help you. Listed below are some of the applications of artificial intelligence in today’s digital world:

Computer Vision Systems

Computer vision analyzes data by using different images that show various objects of interest. It uses deep learning and image processing to recognize patterns and then provides predictions autonomously.

From simple everyday applications such as recognizing human faces to complicated ones such as detecting obstacles when driving autonomous vehicles, computer vision helps AI-enabled technologies and devices to perform their tasks more effectively.

An example of how AI affects computer vision systems is through its use in machine vision systems. A sub-field of computer vision systems, machine vision systems finds their use in automotive applications, such as detecting stop signs, detecting obstacles, etc. Machine vision technology reduces distractions and enables the driver to stay alert while driving.

Creating and Generating Online Content

Who wouldn’t want a machine that writes online content by itself? Although AI cannot write about their opinion for a political blog or its views about new emerging technologies, it certainly can create content for your website that can help attract an audience from every part of the world. It can also help you save money, resources, and a lot of time. You only need to feed it data that it can understand and learn, and it will take care of the rest.

Wordsmith and quill are examples of such programs, which companies such as Forbes and Associated Press use to create new and fresh content for them, leading to numerous visits on their websites. With the use of templates and keywords, these programs generate content readers feel that humans wrote it.

Curating Online Content

AI-based programs not only allow you to create content, but they also help you curate it. It enables visitors to interact with web pages in a better way, only showing them the content they want to see. It helps in providing visitors with more personalized user experience. For example, if you add a product to your shopping cart on Amazon, you will see suggestions relevant to your choice.

“Another example is Netflix. If you watch a movie or a drama serial on it, with the help of AI, Netflix provides you with relevant movie and drama suggestions that piques your interest.” 

From a marketing perspective, imagine showing visitors the content they wish to see. With the help of deep learning and machine learning, you will surely increase your daily clicks.

Email Marketing

Emphasizing user behavior and preferences, companies use AI-based marketing campaigns to make it easier to connect with potential clients. With the help of machine learning, companies can analyze trillions of megabytes of data to find out the time of day to engage with potential clients, what type of content to show them, the email titles and subjects that generate the most clicks, and its frequency.

Wouldn’t you want to know all these so you can save time, money, and effort? Some of the examples of such AI-based email marketing include Persado, Boomtrain, and Phrasee. It will transform how you perceive email marketing and allow you to generate tons of clicks, increasing your online presence.

Digital Advertising

Gone are the days of posting advertisements in the newspaper or the local radio channel. Artificial intelligence has made it easy for companies to find an audience that will be more prone to finding an interest in an advert. A sub-field of digital marketing, digital advertising sees the most benefits when adopting Artificial Intelligence.

“For example, Google Ads and Facebook Ads use Artificial Intelligence and machine learning to find people that will most likely have an interest in your Digital Adverts.” 

With the help of AI, both these platforms analyze user information such as demographics and interests to detect users that suit a company’s advertisements.

Website Design

If you think that a great website cannot exist without the help of a coder or a programmer, then we have news for you. Nowadays, various AI-aided website design programs exist that can easily design a website with the help of images, call to actions, and text provided by the user.

And all that without any need of a programmer or a website designer.It allows companies to save money and makes their website look like someone with a college degree designed it.

Artificial Intelligence Chatbots

Nowadays, brands usually communicate with their potential clients through Facebook messenger, WhatsApp, and other online communication platforms. As everybody already uses these platforms, it provides companies with a quick way to send the word out about their brand. Such a medium of communication leads to a requirement for faster responses. That is only possible through AI aided chatbots.

Chatbots are also available 24/7, which is not possible for a human being. For example, a big brand like Sephora uses chatbots to provide visitors with recommendations and make-up advice, depending on their interests, and without human intervention.

Final words
As you can see, artificial intelligence in the digital world provides numerous benefits, whether in marketing, advertising, or providing a great user experience to a customer. Also, to clarify, Artificial Intelligence is not here to replace human beings, but it helps them perform their task more effectively and efficiently.

However, for something like this to happen, they must give Artificial Intelligence a chance. Otherwise, they risk facing the inevitable.

Also Read Artificial Intelligence Myths and Facts


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AI and Machine Learning Technologies Are On the Rise Globally, with Governments Launching Initiatives to Support Adoption: Report




Kate MacDonald, New Zealand Government Fellow at the World Economic Forum, and Lofred Madzou, Project Lead, AI and Machine Learning at the World Economic Forum have published a report that explains how AI can benefit everyone.

According to MacDonald and Madzou, artificial intelligence can improve the daily lives of just about everyone, however, we still need to address issues such as accuracy of AI applications, the degree of human control, transparency, bias and various privacy issues. The use of AI also needs to be “carefully and ethically managed,” MacDonald and Madzou recommend.

As mentioned in a blog post by MacDonald and Madzou:

“One way to [ensure ethical practice in AI] is to set up a national ‘Centre for Excellence’ to champion the ethical use of AI and help roll out training and awareness raising. A number of countries already have centres of excellence – those which don’t, should.”

The blog further notes:

“AI can be used to enhance the accuracy and efficiency of decision-making and to improve lives through new apps and services. It can be used to solve some of the thorny policy problems of climate change, infrastructure and healthcare. It is no surprise that governments are therefore looking at ways to build AI expertise and understanding, both within the public sector but also within the wider community.”

As noted by MacDonald and Madzou, the UK has established many “Office for AI” centers, which aim to support the responsible adoption of AI technologies for the benefit of everyone. These UK based centers ensure that AI is safe through proper governance, strong ethical foundations and “understanding of key issues such as the future of work.”

The work environment is changing rapidly, especially since the COVID-19 outbreak. Many people are now working remotely and Fintech companies have managed to raise a lot of capital to launch special services for professionals who may reside in a different jurisdiction than their employer. This can make it challenging for HR departments to take care of taxes, compliance, and other routine work procedures. That’s why companies have developed remote working solutions to support companies during these challenging times.

Many firms might now require advanced cybersecurity solutions that also depend on various AI and machine learning algorithms.

The blog post notes:

“AI Singapore is bringing together all Singapore-based research institutions and the AI ecosystem start-ups and companies to ‘catalyze, synergize and boost’ Singapore’s capability to power its digital economy. Its objective is to use AI to address major challenges currently affecting society and industry.”

As covered recently, AI and machine learning (ML) algorithms are increasingly being used to identify fraudulent transactions.

As reported in August 2020, the Hong Kong Institute for Monetary and Financial Research (HKIMR), the research segment of the Hong Kong Academy of Finance (AoF), had published a report on AI and banking. Entitled “Artificial Intelligence in Banking: The Changing Landscape in Compliance and Supervision,” the report seeks to provide insights on the long-term development strategy and direction of Hong Kong’s financial industry.

In Hong Kong, the use of AI in the banking industry is said to be expanding including “front-line businesses, risk management, and back-office operations.” The tech is poised to tackle tasks like credit assessments and fraud detection. As well, banks are using AI to better serve their customers.

Policymakers are also exploring the use of AI in improving compliance (Regtech) and supervisory operations (Suptech), something that is anticipated to be mutually beneficial to banks and regulators as it can lower the burden on the financial institution while streamlining the regulator process.

The blog by MacDonald and Madzou also mentions that India has established a Centre of Excellence in AI to enhance the delivery of AI government e-services. The blog noted that the Centre will serve as “a platform for innovation and act as a gateway to test and develop solutions and build capacity across government departments.”

The blog post added that Canada is notably the world’s first country to introduce a National AI Strategy, and to also establish various centers of excellence in AI research and innovation at local universities. The blog further states that “this investment in academics and researchers has built on Canada’s reputation as a leading AI research hub.”

MacDonald and Madzou also mentioned that Malta has launched the Malta Digital Innovation Authority, which serves as a regulatory body that handles governmental policies that focus on positioning Malta as a centre of excellence and innovation in digital technologies. The island country’s Innovation Authority is responsible for establishing and enforcing relevant standards while taking appropriate measures to ensure consumer protection.


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Is GPT-3 the “Adam” of Natural Language?




OpenAI has long asserted that immense computational horsepower in conjunction with reinforcement learning is a necessary step on the road to AGI, or AI that can learn any task a human can [14].

The fathers of AI 2.0, such as Yoshua Bengio and Yann LeCun, argue that AGI is not possible to create from current AI technology. They think we need self-supervised learning (actually GPT-2 and GPT-3 are self-supervised) and advanced neurobiology-based advancements [15].

However, the fathers of AI 1.0, the grandfather’s of AI, such as Marvin Minsky and Andrew McCarthy, argued that an abundance of knowledge (data) and a “society” of common-sense reasoning specialists was the road to AGI [16].

GPT-3 is the existence proof that scaling up the amount of text (data), scaling up the parameters (model size), and scaling up the training computes results in better accuracy (scary performance) on a specialist for few-shot NLP tasks.

Do the model’s architecture, size of the model, and the amount of training computes realize a common-sense reasoning specialist? Do data and common sense reasoning get us to AGI?

Speculation About a Possible Future of Artificial Intelligence

So like, the biggest mistake that I see artificial intelligence researchers making is assuming that they’re intelligent. Yeah they’re not, compared to AI. — Elon Musk [12].

Sixty to sixty-five years ago, one of the first computers filled a room. Sixty years later, a computer core, about the size of my head, has scaled up about 1 billion times (maybe more) the first computer.

Suppose the first viable quantum computer fills an entire room. Will it be 60 years before a quantum computer core, the size of my head, scaled up about 1 billion times the first quantum computer?


Imagine a quantum computer, with an AGI (Artificial General Intelligence) model of a scale of 1 billion times the parameters of GPT-3 or about 3 million times the parameters of the human brain.

“I’ve predicted that in 2029, we will pass the Turing test,” stated Ray Kurzweil [11].

Note: GPT-3 is real close to GPT-3 passing the Turing test [13].

GPT-3 is quite impressive in some areas, and still clearly subhuman in others. — Kevin Lackey, Just, 2020.

Do you think we will have a Hawking-Musk nightmare or a Havens-Kurzweil dream [11,12]?

We may have both, or we may have neither.

I put money on our tool-making. I doubt we will change, or should we change this behavior.

I feel that Elon Musk, with the NuralLink project, is making a bet on our tool-making about the future potential of AI [17].


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