What is AI?
Artificial Intelligence is usually used to cover any technology with 'human-like aspects or is close to what we perceive to be a robot. This is only half-correct—artificial Intelligence, both the science and engineering machines that are 'intelligent. The emphasis is on software that either mimics or exceeds human intelligence. Some definition below should be helpful to understand the complexity and applicability of AI.
Machine Learning: An AI application that provides the ability to learn and improve from training without being programmed manually. Machine learning focuses on developing programs that can access data from live or synthetic data and use it to learn for themselves. Being trained to look for patterns in data algorithms can make better decisions in the future. The goal of ML is to create systems that can operate and grow without human intervention.
Natural Language Processing: Artificial Intelligence machines process, dissect and understand human language in any context to perform tasks automatically. Such as answer-to-question, summarization, navigation, and machine translation. Analysis of both semantics and syntax is necessary to understand the structure and identify how words relate to each other in NLP.
Artificial Neural Networks - i.e.: 'neural networks' are systems inspired by the biological neural networks that make-up animal brains. Neural networks are connected units or nodes called artificial neurons, which loosely model the neurons in a physical brain.
What is AI Software?
Unlike AI, which in itself is an umbrella term. AI Software can be both defined and concretely related to some technological features. AI software like Artificial intelligence refers to a machine's abilities to resemble or surpass human intelligence with no intervention.
AI software features include Machine Learning, Speech & Voice Recognition, Virtual Assistant, Business Intelligence platforms, etc. Software with AI achieves intelligence levels by learning various data patterns and insights that are constantly being adjusted through algorithm training, creating more 'intelligent software.
Three Types of AI
RPA or Robot Process Automation is the automation of digital and physical tasks - typically administrative tasks, transactions, financial activities, or addressing queries. RPA uses both Artificial Intelligence and Machine Learning to configure and automate tasks in 4 crucial phases - planning, development, deployment/testing, and support/maintenance.
- Planning- gathering process to be automated, test objects, finalizing implementation approach
- Development - the creation of automation workflows
- Deployment/Testing - uncovers unexpected outages and bug-free products
- Support/Maintenance - ensures that the product can constantly be deployed and iterated to create a positive experience.
Businesses that use RPA have seen improvements in Customer Satisfaction due to lower bug risk leading to a lowered operational risk for users. The automation within itself saves time for businesses and delivers consistent results making it a cost-efficient add to any organization's tool stack.
Uses components of artificial intelligence - algorithms or 'deep learning' to detect patterns in vast volumes of data and interpret their meaning for organizations to analyze data and predict outcomes.
Cognitive insights allow analytics to be more flexible and valuable than traditional analytics. By aggregating information more clearly in datasets and training models with different aspects and amounts of data, companies can use data to create personalized strategies for customer profiles and segments.
This technology analyzes customer data droves and can be used to detect identity fraud, investigate claims, speed hiring, automate personalized advertising, more accurate data modeling, and predict trends or buying behavior.
Cognitive Models are helpful because they apply to many areas and flexible since they can learn external and new feedback, allowing stakeholders to make more informed decisions faster.
Cognitive engagement is a type of Artificial Intelligence that uses natural language processing and machine learning to help organizations engage with customers and create personalized strategies. This software can create an overall positive experience for customers when engaging with brands on-top of cognitive insights.
For example, Starbucks has used this technology to not only send 'Birthday' wishes but drive you to purchase their coffee. This allows them to offer benefits to subsets of customers, and they've found that 'by building momentum with customers [this] is generating a 2x higher spend per members'. More than personalization - Cognitive Engagement allows organizations to use AI chatbots and Intelligent agents, which in-turn provide a human-like conversation with customers.
Benefits of AI Software
Unlike Human agents who need to be on the clock, have break times, and can't be working 24/7, AI bots are always available. Customers don't have to abide by the retail hours anymore. Instead, they can hop online and enter the store digitally at any hour. This allows for customers to be met with a guiding hand through their interaction on your site.
Human agents can be both time-fixed but also inconsistent. It's within human nature, and we have human errors in our tasks. AI software is different. They do not make mistakes if appropriately programmed. With AI, decisions are made from the previously gathered data/information from the set of algorithms.
They are leading to a reduction in errors and higher precision accuracy in organizational tasks. Process-oriented tasks such as approving loans, deciding whether a customer should be on-boarded or identifying fraud can be automated. Tooling such as RPA allows for this software to be flexible and autonomous in the workplace to meet company goals.
Handle repetitive tasks with ease
Machine Learning models can take large amounts of data, let's say your past internal data, and predict and suggest the best course of action based on the data. This makes this type of technology extremely valuable to companies who move fast.
For example, a sales organization is growing fast. They can't correctly score leads properly and have no time for emails. A tool that uses machine learning can hop on and help cipher and analyze internal data on previous leads or profiles of closed deals and train the model to rank leads.
Research by Hubspot shows that salespeople spend 33% of their day talking to prospects. They spend 21% of their day writing emails, 17% entering data, another 17% prospecting and researching leads, 12% going to internal meetings, and 12% scheduling calls.
According to that data, it would be reasonable to say that there are many repetitive, easy, and automatable tasks. Tasks such as data processing (entry) or call schedule can be replaced by a tool such as Einstein, Drift, or Chili Piper.
Manage time better
Since we're all so busy, working remotely, and digital-first, we know a full inbox. Sometimes important emails get lost in the inbox, and it is what it is. Tools like Plicca and SaneBox use Process Automation and Cognitive Insight to help professionals prioritize their email inboxes. Tools like this provide a big break to inboxes and help employees find a time that they can use to focus on customer needs.
As a business, there is no time for waste. That's precisely why teams run on different 'styles' like Agile or Scrum. Despite those styles, the reality still stands that human error and human limits stand in the way of being effective. AI software has been helpful for a long time, like CRM.
Using the AI's buyer model and several inputs from the organization, the algorithm can identify a consumer's lifetime value. Tools like Atomic Reach and Ada provide Marketing and Customer Support teams with rich data on both consumers and performance, such as Identifying, understanding, and anticipating spikes in inquiry volume over a certain period or Projecting the number of live agents required during peak seasons and double down with automation for greater efficiency and cost savings.
According to a study by Accenture, organizations can increase employees' productivity by 40% by utilizing artificial intelligence. Automating data collection, analysis, and decision-making can boost profitability by an average of 38%. As employees free-up time from the monotonous tasks that bogged them down, they can focus on more high-value activities such as retaining key accounts, creating content or working on strategy. Data from CMSwire notes that 65% of workers are optimistic and excited about having a software companion. Nearly 25% of employees already report having a loving and gratifying relationship with AI at work.
Reduction of Error
As mentioned, Artificial Intelligence brings many benefits like process automation, but the most impactful is the reduction of error.
Every 1 in 10 hospital admissions leads to a medical error, and 1 in 300 entries results in death due to these medical errors. In 2016 a study by Frost and Sullivan stated that Artificial Intelligence could reduce medical errors by 30-40% for hospitals. However, AI applications have more considerable applicability than only hospitals. Many have found a use for AI and machine learning solutions to screen and detect cancer, analyzing clinical findings, increase access to healthcare, and mine/automate data to improve efficiency and customer experiences. AI models trained regularly become more efficient and help make more educated decisions that reduce human error.
Like in Hospitals, Artificial Intelligence is finding applicability and usefulness in Cybersecurity. In 2018, businesses in the US lost $1.2 billion to business email compromise. 'Phishing' is a practice of sending fraudulent emails to induce the other individual to reveal personal information. You work at a company somebody sends a phony email, and boom, you're in an entire email thread, and now they've got your information. It's happened.
Companies have employed Artificial Intelligence tools to keep employee/customer data safe and prevent any losses from cybersecurity attacks to reduce this standard error. Using anomaly detection, the process of finding patterns of interests within datasets (exception or peculiarities), this software can analyze message content and understand how users communicate in these phishing emails. As AI becomes a stronger all in the cybersecurity fight, it's essential to have models that can efficiently and accurately analyze individuals' behavior and communication patterns.
Examples of AI Software
The ranges and applicabilities of AI software are always changing. According to Gartner, four trends AI features will be potentially disruptive over the next 5 years.
- Augmented Analytics - Uses ML to transform analytics content - the way it's developed, shared and consumed.
- Augmented Data Management - Converts metadata from being used for audit and reporting, which is the primary driver for AI/ML
- Continuous Intelligence - Real-time analytics, which integrates with business operations to analyze data and create action plans in response to future events.
- Explainable AI- Auto generates an explanation of AI models in terms of accuracy, attributes, model statistics, and features.
The examples and companies below are some examples of the current Artificial Intelligence which are hot on the market
Ada is the 'front line' of the customer experience with an AI chatbot that improves CX, reduces costs, and drives revenue - which frees up agents.
Ada is a Conversational AI bot. Conversational AI combines natural language processing (NLP) with traditional software like chatbots, voice assistants, or an interactive voice recognition system to help customers through either a spoken or typed interface.
Ada makes the process easy for companies since they are dedicated to implementations focused on
- Helping conceptualize and socialize conversational AI strategy
- Training and launching conversational AI to ensure its efficacy
- Providing regular feedback and insights to your team, such as understanding what's working well and what can be improved.
Tools like Ada provide a high value for companies and personalize their communication with customers on various channels.
Samsung Nano-Chips: As Tesla looks forward to rolling out their Full Self Driving software, they have decided to partners with Samsung Electronics. Sassmums will be help tesla with high-tech EUV nanochips, which will be hosted in the Tesla infotainment. These AI chips are the core technology to developing an entirely autonomous vehicle by processing information input from all parts of a car and providing it on a screen. In the past, Tesla has created its own AI chips and partnered with NVIDIA in 2017.
Deep Neural Networks- When you place one to hundreds to thousands to millions of neurons in multiple layers and stack them up on top of each other. You get a neural network that can perform very complicated tasks, such as classifying images or recognizing speech. This is how Tesla trains their Model S/Y/X's algorithm to detect any nuances on the road and to learn the more the care is driven.
Alexa - probably the first household AI. Alexa is a conversational AI that uses our voice and language processing to solve problems. Alexa is the all-in-one home solution that can play music, create a to-do list, set alarms, and much more. Rohit Prasad, VP of Alexa AI at Amazon, outlined the capabilities that were recently developed:
- Interaction cues, note errors, and connect them.
- Learn asking to follow-up questions when a gap in knowledge arises about returns and learned modes.
- Deep learning space parsers to understand gaps and extract new concepts.
- Natural conversation and adaption
- Follow-up mode when interacting with humans.
Much like Tesla, the 'chip' or the processor is the key to utilizing Artificial Intelligence technologies' speed and efficiency. As Alexa continues to grow and conversational AI develops, customers will receive a human-like experience both digitally and chatting with a speaker.
Netflix uses a recommendation engine to show you what shows are your favorites. A recommendation engine is a data filtering tool that uses user data and machine learning algorithms to filter, catalog, and predict and curate relevant items and products for the user. Surprisingly, the algorithm is doing something right; 75% of users select movies based on its recommendations. Since Netflix has successfully applied to one use case, they have also applied it to both Artwork personalization and Optimal streaming quality.
The importance of these models and algorithms is quintessential to Netflix's success. It shows the power of Machine Learning and Artificial intelligence when applied correctly - focusing on the customer and their experiences based on taste.
So, what's a nest? A nest is a smart-home company that uses artificial intelligence and machine learning in its products. Their product line is unique - Nest Cam, Nest Protect (Smoke Detector), and Nest Thermostat.
The Nest Thermostat uses unique machine learning algorithms to monitor users' home temperature to get a good reference figure. Eventually, this reference temperature data provides a matrix for the thermostat to learn what a person's schedule is like and how they like their home to feel. The built-in sensors and the user's cellphones' location of the user's cellphones allow the models to operate on schedule accurately.
Siri is an AI-based voice assistant on all Apple devices. The technology resembles and is comparable to Amazon Alexa. Siri has a human voice, reacts to conversational queries, and can learn from recent conversations. Siri uses Machine Learning, Artificial Intelligence, and Natural Language Processing to break down user's commands.
The steps are as follows for Siri: Listen -> Think -> lookup data -> Talk. This is the process for many Language Processing Software.