This guide is an overview of the mega-popular technology, that is Artificial Intelligence. It includes key concepts, use cases, and risks of joining the AI movement.
To dive deeper, let’s define AI. From a business standpoint, Artificial Intelligence (AI) refers to using intelligent machines or systems to solve business problems. The machines can be trained to perform tasks by processing a massive volume of data to identify patterns.
СTO Vision names several elements that are the enablers of a good AI case. They are divided into two groups: technology and business.
According to the IBM report, some areas that benefit from AI implementations. We can find good AI use cases in all industries. But IBM singles out several domains where AI has a great potential.
Other points to consider:
AI is a viable solution to many problems. Its benefit is achieved in the three domains we described previously. Now let’s have a look at the successful AI use cases from IBM.
Production of goods: a neural network detects aberrations in the work of machines. The network is trained using data received from sensors placed on the truck engines to identify the deviations from the norm.
Automotive industry: predictive error detection for welding robots and predictive maintenance assessment. Supervised learning techniques are used to develop predictive models to serve as early warning systems for failures. They continuously receive system messages. This way, the manufacturers can do maintenance work when needed to reduce the downtime, false alarms, and unnecessary workload.
Energy suppliers: A microgrid forecast of energy demand and production optimization. Together with predictive mathematical optimization, predictive ML models show which the power sources will be the most cost-effective.
Raw material supplier: An informative dashboard. Together with the customer’s procurement experts, IBM analyzed the business dynamics and made a list of potentially relevant data sources. Several models for machine learning were trained to learn price fluctuations and predict future price changes.
While implementing AI can change your business model, there are many risks to it.
Here are the main AI technologies your company can benefit from, according to CIO.
This subset of AI is the foundation of any organization’s AI strategies. Indeed, it’s proven to bring a lot of value, especially in complex solutions. The solution includes algorithms, development tools, APIs, and model deployment, and many more. Machines are taught without explicit programming.
Another approach to data resembles how the brain works and uses artificial neural networks. The information is flowing through these networks, altering their structure. As such, they can change (and be “trained”) depending on the input and output. The learning is based on observing the data sets. Deep learning is a growing trend that fits in well with Big Data. In particular, it presents value to pattern recognition and classification.
Natural language processing
This technology has to do with the interaction between humans and machines. Computers can analyze and understand spoken and written language. Voice assistants such as Alexa, Siri, and Google Assistant all use NLP to handle voice requests. NLP is broadly used in customer service and support as chatbots and voice assistants, but can also bring a lot of value to an organization’s internal processes.
Natural language generation
The software-based on this technology turns all kinds of data into readable text. This technology can automate business analytics, product description, finance reporting, all sorts of reminders, and more. All in all, it lets you create content instantly. Structured data can turn into text with high accuracy and at a record speed of several pages per second. Companies like Automated Insights, Lucidworks, Attivo are worth watching.
To many people, the difference between a virtual agent, a virtual assistant and intelligent virtual assistant is merely semantics. Some try to distinguish between them and regard virtual agents as customer assistants, while they are more akin to online assistants in reality. Virtual agents are often known as AI-induced bots that can lead a meaningful conversation with a user. One advantage is that the clients can be assisted at any time. But they also have a great potential in other fields such as healthcare and heavy industry.
These include devices with embedded AI, microchips, and GPUs. The combination of hardware and AI goes beyond consumer devices such as games and entertainment. Hardware with embedded AI will be used to advance deep learning. Key players to watch are Google, IBM, Intel, Nvidia.
Robots that are controlled by AI programs and are used to automate manual work. This technology is mainly used at warehouses and factories now but showing great potential in medicine as well as business and smart homes.
Robotic Process Automation
RPA automates business processes intending to redistribute the human resources to perform more complex tasks that bring more added value. It is a technology that allows organizations to configure software (software robots) to perform repetitive, mechanical operations at the user interface level. Software work performs tasks in the same way that people do, receiving, sorting, processing data, and performing specific actions with them, without changing the organization’s IT landscape.
Supervised Learning. One of the machine learning methods, where data labeled by humans is fed to a machine so it can be taught. A ready model is used to provide predictions from new data.
Unsupervised Learning: Opposed to Supervised Learning, models of this type detect patterns in unlabelled data and where outcomes are unknown.
Training Data: An initial set of data used to develop a model. It helps a program understand how to apply technologies like neural networks to learn and produce results.
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Machine learning is in its heyday, but the question for many businesses remains: How can it solve the existing problems? In this post, we will look at business areas in which ML could make a good case. Hopefully, it helps you see what can be done so you can figure out how it might be applied to your business.
Machine learning is applied for complex decisions where a lot of factors are at play. That said, problems that suggest a clear chain of steps to follow in order to resolve them aren’t likely to need machine learning at all. The same goes for having existing cases of successfully addressing problems using ML. In these scenarios, applying algorithms may be a redundant step.
On the other hand, here’s a list of common business cases where leveraging machine learning is recommended.
It’s somewhat problematic to come up with a set of rules for the face recognition process, given that there are different nuances like skin and eye color, shapes, and angles that might impede accurateness. Nevertheless, machine learning algorithms can be trained to detect eyes, mouth, nose, etc. Facebook already uses those, and there are also many free face recognition tools.
Email spam filters go by simple rules, for example, they can identify and block compromised domains and IP addresses. However, these algorithms tend to lack in filtering incoming messages based on a user’s personal preferences. That is where applying machine learning can be the right solution.
The tastes of each person in music, movies, and products are different. Tech giants like Netflix, Spotify, Amazon rely on machine learning and data to predict what a user might prefer.
Machine learning makes it possible to translate a spoken language into text. The significant challenges are a variety of accents, loud environments, and the fact that speech recognition algorithms need lots of data. The top companies operating in this area are Nuance Communications, Google LLC, Amazon.com, Inc, Apple, Inc, IBM Corporation, Microsoft Corporation, and Baidu.
It’s impossible to predict which Facebook ads will prompt a particular user to click. Machine learning can identify patterns in user behavior and determine which ads resonate with which group of users.
Little cases of fraud detection can be fended off with a clear-cut set of rules. That is because fraud techniques are continually evolving, so your protection needs to be adapted all the time.
Machine learning systems can process data that concerns your buyer profile and types of things they are looking to buy from you. You can tweak the algorithm to target clients that are land concrete pages of your website. For example, if you’re selling phones, you can use machine learning to sell iPhones to people that have reviewed your Apple product articles.
This one is a good area for implementing machine learning. Instead of offering a fixed price at all times, you could tie pricing to time of the day or a type of user. A good example here is Uber. Its algorithm analyses a destination a user wants to go to and nearby routes to offer a special price.
Machine learning and customer service are a natural fit. Lots of companies have already improved their services using chatbots to address customer requests in no time.
There’s no denying it, chatbots boost sales. That can be backed up by the case of Sephora, who increased their sales up to 11% after integrating a chatbot assistant into their Facebook page. The secret is to create a chatbot that would capture your brand voice while providing a service akin to a human assistant.
According to EmerJ blog, several factors warrant the usage of machine learning at an organization.
Recent and accurate data
Clean data is the cornerstone of any data science effort. But it also needs to be up to date, which is particularly true for the rapidly changing fields. If your data is not recent, it can hardly present any predictive value. From years of operating, you probably have gained a large volume of data. It needs to undergo the cleansing process before your data science team can use it.
Unsupervised learning can be applied in many cases of unlabeled data, but it’s not the best decision to start out with unsupervised ML. More profit can be gained from old labeled datasets.
Facebook owns a vast amount of labeled data like users’ faces from various angles, in different poses and with different expressions. The company uses this data is used to train algorithms, making them highly accurate. Another case here is Google that analyzes the relevance of search results based on the rates of clicks, the usability of pages, user scroll depth, and other factors. There cannot be a set of clear rules for showing the right results with such a number of factors at play. Supervised datasets are also vital to credit companies. While there’s a huge amount of transactions, it’s possible to track connections in purchases, locations tied back to individual users. Thus, it’s possible to predict fraudulent actions. Machine learning can also streamline customer support by identifying and marking types of requests received via emails or phone, such as technical problems, refunds, shipping problems, and more.
Room for error
As suggested by EmerJ, machine learning cannot be applied in an environment where errors are intolerable. After all, machine learning is a malleable, adaptive skill that continues to grow as the company endures. An example of such is a system that recognizes an amount or a company name on an invoice and then pays it. An error in such a case results in overpaying the amount or paying the wrong company. In this example, it’s best to apply machine learning to sort types of bills, but the payment part requires human involvement.
The company has long since implementing ML algorithms to improve user experience. Pinterest uses image search and recommendation scripts developed by Kosei, a machine learning company. Additionally, the social medium leverages various algorithms to filter content as well as for monetization purposes.
The social media giant uses machine learning to take your communication with chatbots to the next level, making it more human-like. There are also algorithms for filtering out spam and irrelevant content.
A global giant named IBM is also mastering machine learning. The most prominent project is IBM Watson used in healthcare.
The company uses algorithms to organize the news feed so that users can see the most relevant content. Analyzing tweets and likes of each user helps to achieve that.
Through machine learning, the behemoth improves search results and page ranking and boosts speech recognition and translation speed.
Baidu, a Chinese search engine, strives to keep pace with Google, investing heavily in machine learning. One of the most significant developments based on machine learning algorithms is Deep Voice, a human voice technology capable of generating such natural voices that are barely distinguishable from natural ones. The technology can even “clon” voices, pronunciation, stress, timbre, and pitch of sounds in spoken language.
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If you see the benefit of using machine learning to improve your business, it’s useful to consider a few things. Starting out with machine learning doesn’t necessarily take an in-house team of specialized experts. Algorithms can be created by service providers or software developers with knowledge of data science libraries. What you can do on your part is to ensure that the data is recent and accurate. Think about tasks that can be automated to increase speed and scope – that area can be improved by machine learning. Above all, consider that machine learning isn’t there to put humans out of work. Instead, it can be used to reduce routine work, boost speed and improve accuracy.
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Artificial intelligence is one of the most significant technologies of recent times. From solutions that recognize symptoms of a heart attack to face aging apps and voice assistants. AI has penetrated every field: medicine, social media, entertainment. A lot of companies, large and small, turn to AI in the hope of expanding the possibilities of their business. The decision to implement AI solutions comes with the need for a relevant framework. In this article, we are going to review 7 popular frameworks for AI.
TensorFlow is used for voice and image recognition as well as for language processing like Google Translate. Works best for complex projects, including multi-layer neural networks. DeepMind, Uber, Airbnb and Dropbox use this platform.
Tensorflow has API in Python on top of C and C++. The framework has a flexible ecosystem of tools and libraries as well as a vibrant community. Tensorflow enables data scientists to use the most recent Machine learning tools. Developers can create and deploy machine learning applications
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TensorFlow comes with intuitive API interfaces (for example, Keras) that enable fast model iteration and simple debugging. As a multiplatform solution, it allows to teach and deploy models in cloud and on-premise regardless of the used language.
PyTorch was developed by Facebook and used by Twitter and Salesforce among others. It’s a good solution for when you need to teach models fast and efficiently.
PyTorch is an environment for machine learning written in Python. It enables Tensor calculations with GPU acceleration.
With PyTorch, data engineers can change the network behaviour on the go. The framework offers dynamic calculations graphs that allow you to process input and output of a variable length, which is useful if you are dealing with recurrent neural networks. Developers can use C and C ++ with the help of API extension for Python.
Unlike TensorFlow, PyTorch is less flexible in terms of supporting multiple platforms. It lacks built-in data visualization tools but has tensorboardX as an external alternative.
When deploying networks on the GPU, PyTorch will take up only the necessary video memory.
Keras is a minimalistic deep learning library in Python that runs on top of TensorFlow, Theano, or CNTK. It is compact, modular and expandable solution that is aimed at accelerating work with neural networks. Keras is chosen for smaller projects due to mediocre performance as compared to Tensorflow.
Keras supports a wide range of neural network layers: convolutional, recurrent, and dense.
The library contains numerous implementations of widely used building blocks of neural networks: layers, target, and transfer functions, optimizers, as well as a variety of tools that simplify work with images and text.
Keras is one of the best deep learning frameworks for translation, image and speech recognition.
A deep learning framework in C++ with Python interface. Caffe is characterized by expressiveness, speed and modular structure. Caffe is a good solution for training models, processing images and improving neural networks. It also supports CNN and feedforward neural networks. A few years ago, Facebook released Caffe 2, which is suitable for both mobile and large-scale development in the production environment.
Deeplearning4j ( DL4J) is a good choice for Java programmers. It is a commercial-grade, open-source, distributed library written for Java and Scala. DL4J provides good support for various types of neural networks, from convolutional to recurrent and recursive. This framework is used for image recognition, natural language processing, vulnerability search and text analysis.
XGBoost is an open-source framework that offers a gradient boosting system for C ++, Java, Python, R, Julia. It boasts high performance, flexibility and portability. XGBoost belongs to the realm of classical machine learning frameworks.
Originally, it was a research project of Tianji Chen and Carlos Gestrin as part of the Distributed [Deep] Machine Learning Community, but later it was expanded and presented to the public at the SIGKDD conference in 2016.
XGBoost focuses on computational speed and model performance and is a suitable solution for solving regression, classification, and sequencing tasks. If the data can be presented in the form of a table, then the accuracy and performance will be significantly higher than that of DeepLearning solutions.
The framework is compatible with Windows, Linux and OS X. It also supports AWS, Azure and Yarn clusters, works well with Flink, Spark.
One more Python deep learning framework supported by Intel, IBM, NVIDIA, and AWS. It’s a flexible, intuitive solution that runs on multiple GPUs with little effort, supports CUDA and various networks such as feed-forward convnets, recurrent and recursive. This framework is mainly used for voice recognition, machine translation, and key analysis.
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Years ago the main communication channels boiled down to phone calls and meetings. Then, the internet has brought us lots of other options, like email, social media, mobile and web apps. The most recent shift in the way people communicate with businesses is the chatbots.
A chatbot is an AI-powered software that is programmed to simulate a conversation with a real person, be it on a website, app or platforms like Slack, Skype, Facebook Messenger, WhatsApp, and Alexa. Chatbots let customers communicate with brands via text or audio messages and can be easily customized to the needs of any business.
AI-powered conversations resonate with a vast market of Gen Z and young millenials who are extremely comfortable with chatbot interactions. As such, chatbots are becoming not only a communication means but also a sales channel.
If you are looking for a way to apply artificial intelligence chatbots to supercharge your marketing, here’s what this technology can do for you.
Customer service is the chatbot technology’s natural habitat.
Users expect to find information about your product or service quickly and easily otherwise they leave for good. For a small team 24/7 support can be an insurmountable task as well as for companies with millions of customers. Chatbots can be a great solution for both cases.
Salesforce’s research has found that a bot can be a great alternative to a standard contact form, given that 86% of customers would prefer the first option. Take a look at the eBay bot.
image via techcrunch
Make sure your responses are friendly, written in simple language and resonate with your brand’s tone of voice. In addition, you can customize your chatbot by adding a pinch of character – select an avatar image and come up with a memorable name. This will create a sense of communication with a real person. Chatbot applications can help you:
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If want to know about your customers more, chatbots can make things easier for you.
Just like chatbots can be programmed to interact with customers, they can be also be trained to collect data and monitor users’ behavior on your website. This data provides valuable insights into your customers’ needs and how their purchase decision is made.
Combined with deep learning, you can use the information on how users interact with your website to enhance the way they work. For example, by reviewing the questions and requests that a chatbot failed to handle, you can prepare new triggers and assign relevant responses.
Those who have expressed interest in your product or service can potentially become your customers. This can be a long journey, and chatbots can offer a solution that might take the headache out of it. Chatting is the best way to understand the readiness of the lead to make a purchase, be it with a bot or a human. But rather than filling out a bunch of forms, your customers can get a personalized experience via conversation. You just need to program the bot with a set of relevant questions to understand at which stage of the sales funnel your lead stands. If done right, verifying leads with chatbots saves you time while providing an excellent service to your potential customers.
A great application of chatbots is remarketing or follow-up. How do chatbots work in this area? It’s simple. You can set up a chatbot to engage people who have visited your website or social media accounts in a conversation. Chatbots for remarketing is a much better way to reach your potentials as compared to sending non-personalized messages via messenger or email.
While chatbots can’t fully replace email marketing, they can be a powerful tool that helps you get a customer’s response faster than you would with email. Let’s compare a chatbot and email marketing for promoting products and services:
Chatbot: if a recipient is active, a message can be received and seen immediately.
Email: There’s a risk of getting into a spam box where a user might never come by your email. Besides, many clients go through their inbox once in a couple of days. As such, chances are high that it will be days before a lead sees your email.
Chatbot: The chatbot might be able to handle simple, mundane tasks like providing the answer or sending the user to the relevant resource. If the bot can’t solve the user’s problem, a manager steps in.
Email: Your emails can include features such as order or pay online and links to outside resources. When clicking on links and buttons, a new page usually opens, which may be inconvenient for smartphone users.
Chatbots can collect and store customer data by asking questions and analyzing the responses. Chatbots can use collected data in further conversations.
Email campaigns are usually set up according to previously collected data. An email marketing specialist collects insight from campaigns to finetune the strategy for the future.
The outcome? With chatbots, you stand a better chance that your message will be read by the user. More so, you can increase the volume of marketing conversations in messengers. And finally, based on data that comes from users’ engagement with a chatbot, you can set up more targeted campaigns.
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Users prefer personalized advertising messages. Chatbots can bring in the personalized conversational element that can help you build a relationship with your potentials at the early stages of the sales process.
The key to personalized engagement here is to ask customers about their preferences to further turn those into a personalized experience.
A great example of such engagement a chatbot launched by Sephora on a messaging app Kik.
The chatbot is programmed to understand the customer’s needs and guide them to the relevant cosmetics.
In a similar way, CNN and Fox have adopted chatbots to create and deliver a news feed tailored in line with users’ preferences. They have implemented it on Facebook Messenger, Kik, and Line platforms as well as on Alexa, Amazon’s voice-controlled device.
Both are great examples of adopting chatbots to deliver a personalized experience. In fact, lots of Facebook chatbots can be easily set up to serve that purpose.
According to State of Chatbots 2018, chatbots’ main advantages are as follows:
We can conclude that clients are looking to get a personal experience, no matter who will provide it for them. That being said, marketers can use chatbots to hit both targets: to automate the marketing process while providing that personalized experience. Chatbots can also play a key role in the sales funnel and be the source of valuable customer data you can use to build your marketing strategy.