How science-fiction AI has become reality

| September 11, 2019

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When many of us were growing up, science-fiction books, movies, and television shows explored what was possible using science, technology and intelligent computers. These dreams are now becoming reality. Let’s take a look at some of our favorite real-life manifestations of AI in fiction.

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Datacom Systems

Datacom Systems is a leading manufacturer of Network Taps, Network Packet Brokers, Data Aggregation Tools and other network access devices. Since our founding in 1992, we’ve built a reputation for quality engineering and unmatched customer service.

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What Makes an Application “Modern” Anyway?

Article | August 9, 2020

Just because something is labeled a “thing,” in this case DevOps, doesn’t mean that an organization is doing anything modern in its application development practice.Many people will think that technology decision points drive the “modernization” of applications. To be sure, technology plays a key role, but I prefer to think of application modernization as a three-legged stool.

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WHAT DOES FACEBOOK’S QUITE AI ACQUISITIONS ACROSS UK SIGNIFY?

Article | February 17, 2020

The social media giant Facebook has always been at the forefront of AI advancement. Amid all the controversies and roadblocks in its strive to attain AI leadership, the company is moving forward with innovation and tech developments. These developments are a major result of its acquisitions; small but significant. Facebook’s M&A activities are proving to be quite beneficial in its AI journey. Recently, the company acquired Scape Technologies which is a London-based computer vision startup working on location accuracy beyond the capabilities of GPS. Full terms of the deal remain as yet unknown, although a Companies House update reveals that Facebook Inc. now has majority control of the company (more than 75%). Further, a regulatory filings show that Scape’s previous venture capital representatives have resigned from the Scape board and are replaced by two Facebook executives.

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AI and Marketing Automation: The Future of Content Creation

Article | July 23, 2021

The content market industry is growing rapidly. The US spent almost $10 billion on content in 2016. And with the merge of technology and content, this figure has multiplied several times. As a result, humans can concentrate on creativity while machines assist them in beautifying their work. The machines that we are going to talk about are AI-driven content marketing platforms for content creation, curation, and distribution. We will also let you know whether the world of writers is going to be extinguished or not! Content creation needs to be unique. There are numerous articles, blogs, listicles, etc., written on the same topic. So what do you need to do to make your content stand out? You might be thinking of SEO, personalization, target audience, and error-free content. But when all this is carried out manually, it turns out to be a daunting task. What if we tell you that the AI industry has revolutionized the content sector. Machines assist in content creation, curation, personalization, prediction, SEO analysis, keyword search, and everything you can think of. What is AI Content Creation? More than 2.5 quintillion bytes of data are created every day! Imagine the uniqueness your content needs to define to stay ahead in the race. The competition is unbelievable! This is where AI and automation meet content marketing. AI content creation helps to create, polish, personalize, distribute, and market user-centric content. AI uses predictive analysis, natural language process (NLP), natural language generation (NLG), and business intelligence to understand a buyer’s journey. AI helps in creating an efficient content strategy for the writers. It also assists them in using the right keywords and publishing on the right platforms. Moreover, content that is checked by the AI content creation algorithm has the least or no possibilities of errors. Below are the benefits of AI content creation and how it plays a significant role in shaping the future of content writing. Benefits of AI content creation Predictive Intelligence Predictive Intelligence is predicting the customer’s behavior and serving them precisely what they are looking for. It also helps the buyer to navigate to the things exclusive based on their browsing patterns. For example, if a customer is looking for casual shoes and has put the chosen ones in the cart, the AI-powered algorithm will suggest some socks to go with the shoes. The algorithm is designed so that based on the shoe size, color, and brand, it suggests the best pair of socks! And when this role is played in content creation, predictive intelligence suggests words, sentences, synonyms to create excellent content. When you have a process that foretells you the buyer’s journey, it becomes easy to create personalized content and yield the best results. Data-Driven Insights A study has proved that 90% of the customers get converted based on personalized AI content. AI-driven content marketing tools help gather all the customer's activity online and then give them the relevant suggestions. AI content creation tools compare your content with the competitor’s content and your previous works and performances. Based on that, it predicts the content to be framed. Finally, it recommends words and phrases to frame innovative, data-driven content. AI content creation helps in increasing your brand value and reach your target audience effortlessly. Chatbots for Customer Service Chatbots as customer service representatives are immensely successful. Chatbots are like a virtual friend that meets the customer. They not only provide information but also interact with the customers for fun. Chatbots are trained to have a conversation just like humans. They ask about feelings and then have a one-to-one conversation about ways to feel better. For example, when a chatbot is used to book a test drive for a car, it notes the personal details and saves the date for the concerned person and the customer on the calendar. That is pretty much robotic. But in the end, it tells you to have a pleasant drive and not to forget to wear your seatbelt! This is where it connects with the customer. Thus, chatbots can help promote content, navigate customers to the correct pages, and are more likely to fulfill the conversion goal. Examples of AI Content Creation Applications Twinword Twinword is one of the examples of AI-powered keyword research tools. It speeds up the keyword research process and provides the most curated list of LSI, long-tail, short-tail keywords. It understands the customer’s intent and provides you with the target keywords. Articoloo It is one of the Automated AI Content tools used to write articles, blogs, and other relevant content. It has been said to deliver content with the most competitive keywords and a proper understanding of the industry dynamics. In addition, it creates high-quality content which is almost 90% unique. Grammarly The AI-powered tool does more than grammar checks. It checks your article's tone and gives you options to improve or change words, sentences, length, etc., according to the audience. It frames sentences, gives synonyms, and also creates your writing graph. It shows how your writing has evolved with time and keeps on assisting to improve your writing. This is one AI tool that has been widely used by writers and is a proven success. MarketMuse This tool analyses your content and makes it ready for the competitive market. It minutely analyses your content for SEO and content research and gives appropriate suggestions for keywords and content optimization. It also has a keen eye on keyword intensity. Thus, it is the best tool to automate content marketing and keep you ahead of the race! Onespot This tool provides excellent AI Content and User Experiences across multiple platforms. It tracks the buyer’s journey, analyses the user’s behavior on your website, and then automates a personalized experience for the customer. In addition, it generates e-mails and newsletters for the customers for an enriched experience. All the content generation is very personalized and connects readily with the target audience! These are some of the content creation services for optimization, creation, and keyword research. But there is a plethora of AI in content marketing and automated tools for content creation to choose from! Will AI replace human writers? No. AI content creation is still in the infancy phase. It can create newsletters, small articles, or emails but not valuable content like humans. However, there are AI content creation tools that are said to write almost 800 articles in a year. So if you are looking only at content generation, then yes, AI performs a better job than humans. But can machines feel and express the emotions that humans do? No matter how many trillions of words they have fed themselves, expressing emotions with the right words is still a human thing. Likewise, there are stats to prove that humans tend to take action when they feel emotionally connected. And we all know machines are practical beings! So as long as humans are social and emotional, there is no way that AI and automation can replace writers now or in the long run. It can assist humans in writing better quality, personalized and well-researched content, but never replace them. If you still have second doubts, think, did the invention of smartphones with excellent cameras, photo studios, or design studio apps like Adobe photoshop replace photographers? So relax, use AI and automation to minimize your efforts, gather data, and give you a piece of structured and well-researched content. Then use your creativity skills and enhance them by giving the human touch because that is what only a human brain can do! Frequently Asked Questions What is AI in content creation? AI is automated Insights in content creation. It helps optimize the written content according to the target audience. It uses Natural Language Generation (NLG) to create content and narratives. In addition, it can automate repetitive tasks. Once AI creates the content, a human writer can enhance and personalize it accordingly. How does AI help in content creation? AI provides an in-depth analysis of the content, website, and target audience. Based on this research, creates and suggests changes in the content. This in-depth research of AI helps deliver valuable and personalized content to the target audience with the right set of keywords. How do you automate content creation? Content creation can be automated with the use of automation tools. These tools recommend the topic, keyword, and tone of the article to be written. In addition, they assist the writer with the correct grammar, synonyms, and keyword predictions. If it is a technical article, the automated tool can write the entire article with no errors. Examples of such tools are Articoolo, Grammarly, Onespot, etc. { "@context": "https://schema.org", "@type": "FAQPage", "mainEntity": [{ "@type": "Question", "name": "What is AI in content creation?", "acceptedAnswer": { "@type": "Answer", "text": "AI is automated Insights in content creation. It helps optimize the written content according to the target audience. It uses Natural Language Generation (NLG) to create content and narratives. In addition, it can automate repetitive tasks. Once AI creates the content, a human writer can enhance and personalize it accordingly." } },{ "@type": "Question", "name": "How does AI help in content creation?", "acceptedAnswer": { "@type": "Answer", "text": "AI provides an in-depth analysis of the content, website, and target audience. Based on this research, creates and suggests changes in the content. This in-depth research of AI helps deliver valuable and personalized content to the target audience with the right set of keywords." } },{ "@type": "Question", "name": "How do you automate content creation?", "acceptedAnswer": { "@type": "Answer", "text": "Content creation can be automated with the use of automation tools. These tools recommend the topic, keyword, and tone of the article to be written. In addition, they assist the writer with the correct grammar, synonyms, and keyword predictions. If it is a technical article, the automated tool can write the entire article with no errors. Examples of such tools are Articoolo, Grammarly, Onespot, etc." } }] }

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AI Adoption: an advanced digital transformation process

Article | May 17, 2021

Common view is that AI software adoption is 'on its way' and it will soon replace many jobs (example self-driving cars with drivers etc.) and the majority of companies are starting to embrace the efficiencies that AI brings now. Being a practitioner of AI software development and being involved in many projects in my company AI Technologies, I always found my direct experience in the field in contrast with what the media generally portraits about AI adoption. In this article I want to give my view on how AI projects affect the work dynamics into clients work processes and compare that with the studies available on the impact of AI and new technologies on work. This should help the reader, especially if he is an executive, to set the right expectations and mentality when he is assessing the potential investment into a new AI project and if his company is ready for it. To start with, any software development project, including AI, can be summarized into 3 stages: proof of concept (POC) when the prototype has been built, product development when the software is actually engineered at scale, live support/continuous improvements. It occurs often that projects in AI will not go pass the POC stage and this is often due to 1) not right IT/data infrastructure in place 2) not specialist people have been hired to handle the new software or digital transformation process has not been planned yet. Regarding point 2, the most difficult issue is around hiring data scientists or data/machine learning engineers because many companies struggle with that. In fact, in a March 2021 O’Reilly survey of enterprise AI adoption, it has been found that “the most significant barrier to AI adoption is the lack of skilled people and the difficulty of hiring.” And in 2019 software it has been estimated that there were around 144,000 AI- related job openings, but only around 26,000 developers and specialists seeking work. Of course hiring an internal data scientist, it is not the only problem in restructuring the workforce. Often a corporation has to be able to re-train entire teams to be able to fully benefit from a new AI software. I can give an example. As many readers know a sales process involves 3 stages: lead generation, q&a call/mails with potential clients and deal closing. Now, a couple of years ago AI Technologies had been engaged to automatize the q&a call stage and we build a ai bot to manage the 'standard questions' a potential client may ask (without getting into the details, using AI and technically word3vec encoding, it is very possible to automate mails/chatbot for 'standardized questions' like 'how much it cost?' 'how long is the warranty for' etc.). Using this new internal solution, it meant the team responsible for the q&a would have been retrained either to increase the number of leads or the number of closing. The company simply decided to not embark into the transformation process required to benefit the new AI adoption. This example, in various forms, it is actually quite common: companies unless they are really innovative prefer to continue with their corroborated internal procedures unless some competitors threat their profitability. This bring to the fact that actually AI is not an out of the shelves solution which can be plugged in with no effort. As the moment a POC is under development it should be a good norm to plan a digital transformation process within the company. Also it is worth mentioning that, it is unlikely that the workforce has to be dismissed or made redundant as many expected following AI adoption. Just following the example above, what the AI bot does actually is to get over the repetitive tasks (q&a) so people can do more creative work engaging more clients (lead generation) or convincing to buy ( deal closing). Of course, it means that some people have to be retrained but also means that with the same people, you can close/generate more sales. It is a misconception to think that AI solutions will make human work redundant , we just need to adapt to new jobs. My example resembles a classical example on adoption of ATMs. When ATMs were introduced in 1969, conventional wisdom expected the number of banking locations to shrink, but instead, it actually made it possible to set up many more of them, it became cost-effective. There were under 200,000 bank tellers in 1970, but over 400,000 a decade later. The other common problem to face when companies want to embrace AI adoption (point 1), it is their current infrastructure: databases, servers, and crm systems have to be already in place. To put it simply, any AI system requires data to work with so it naturally sits on top of data infrastructure in day to day business operations. In the last two years AI Technologies has been engaged to work with a large public organization (70,000 employees) to build a solution to automatically detect malicious behavior of its employees manipulating their data. To build the AI software we had also designed a system to stream data from each employee terminal into a central database for processing. This infrastructure was not present at the beginning of the project since before the need for malicious detection was arised, the organization never really realized the necessity to gather certain data: a simple login and logout time was all the needed to monitor the activity of their employees (which company folder/file they accessed etc. was not important). This is a common situation and most of the companies' infrastructure are usually not ready to be used directly with AI solutions: their current infrastructure was simply designed with other objectives in mind. For sake of completeness, most companies decide to invest their internal resources in other areas of the business rather than crm or expensive data structures. There is no blame on this choice, at the end any business has to be profitable and investing in infrastructure is not always easy to quantify the return of investment. If anything, this article should have given an idea of the major pitfalls approaching AI projects which can be summarized as follows: • AI solutions are not out of the shelves , ready made software that can be immediately put in use: they often require new skilled hires within the client organization and potentially a plan how to re-utilized part of the workforce. • It is often a myth that AI solutions will necessarily replace the employees although it is possible that they have to be retrained. • Any AI project works on data and infrastructure which are necessary to benefit the new solutions. Before embarking on AI projects an organization has to either budget in a new infrastructure or at the very least an upgrade of the one in use. In essence, due to the implication on both employees and infrastructure, AI adoption should be considered as a digital transformation process more than a software development project. After the overwhelming hype of attention of the recent years, I would expect that in the next 2-3 years more companies will start to realize what AI projects really are and how to best use them.

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Datacom Systems

Datacom Systems is a leading manufacturer of Network Taps, Network Packet Brokers, Data Aggregation Tools and other network access devices. Since our founding in 1992, we’ve built a reputation for quality engineering and unmatched customer service.

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