The future of decision-making includes an inventive blend of information, analytics, and artificial intelligence (AI), with the perfect scramble of human judgment. The outcome is augmented intelligence, where the analytical force and speed of AI assumes control over most of data processing, controlling human workers to make progressively agile, more intelligent choices and find new discoveries.

The development of analytics has caught the consideration of the heads of significant organizations. However, regardless of progressions throughout the years, few have had the option to stay aware of how to utilize analytics and AI among employees, in processes, and with appropriate oversight. The outcome is a lot of smart thoughts and technologies, however, applications that miss the mark regarding their potential.

Today, you can plug data into AI, and it can make its own models and make predictive recommendations. In any case, these models don’t exist in a vacuum. They include data sources and outputs that impact the rest of your business. You need to consider how these models fit in and how to organize the insights from data. Also, you need governance over augmented intelligence to see that the automation is working and individuals know their job in the new man-meets-machine workforce.

If you are putting resources into analytics and AI, at that point think beyond data and technology. You need bilingual ability to bridge the gaps between industry and technology, and build up the correct solutions. You additionally need to consider the change in perspective in how man and machine will cooperate. At exactly that point would you be able to present a winning application and get the most value out of investments.

What is Augmented Intelligence?

Augmented intelligence is an elective conceptualization of artificial intelligence that centers around AI’s assistive job, accentuating the fact that cognitive technology is intended to improve human intelligence instead of supplanting it. The decision of the word augmented, which signifies “to improve,” strengthens the job human intelligence plays when utilizing AI and deep learning algorithms to find connections and take care of issues.

Platforms that offer Augmented Intelligence can accumulate a wide range of data (both structured and unstructured) from numerous sources, across different and siloed systems and they present that data such that gives human workers a total 360-degree perspective on every client.

The knowledge extricated from that data and introduced to the client is deeper and more noteworthy than ever before. Thus, workers are better educated on what’s going on in their industry, what may influence their clients and the opportunities or threats that may emerge. Joining this abundance of data with the human touch is the thing that makes this innovation so incredible.

Some industry experts believe that the term artificial intelligence is excessively firmly connected to mainstream society and sci-fi, driving the overall population to have unreasonable feelings of trepidation about AI and unlikely assumptions regarding how autonomous robots and other smart frameworks will change the working environment and life in general.

Researchers and marketers trust the term augmented intelligence, which has an increasingly neutral connotation, will assist individuals in understanding that AI programming will just improve products and services, not supplant the people that use them.

Most companies have hills of information yet hardly any insights to advance positive business results. They struggle to accomplish ROI, for example, revenue growth, better customer experiences, and regulatory compliance, as well as to build an analytics capability for the future.

Augmented intelligence joins the strengths of people and machines when prospecting a value from data. To be specific, you can augment human instinct with smart algorithms that give quick, information driven predictive insights. These insights can assist individuals with overhauling functions, detect patterns, find strategic opportunities, and turn data into action.

Planned to extend human cognitive abilities, augmented intelligence is not quite the same as straight automation. Looking at the situation objectively, most procedures later on will be intended for straight-through processing, where there will be no people engaged with the procedure. As of now, that is beyond the realm of imagination on the grounds that in 25-30% of cases, you need people to step in.

Consider an airplane autopilot. In present day aviation, the autopilot can work freely, controlling heading and altitude, or it very well may be combined with a navigation system and fly pre-programmed, when the airplane has effectively gotten airborne. You despite everything need a pilot for departure and landing, for the time being. Ideally, an autopilot framework would consolidate human knowledge, in addition to experience focused through the prism of intuition. The outcome would be human judgment broadened by means of augmented intelligence.

To completely welcome the advantages and capability of augmented intelligence in analytics, it is important to totally re-engineer your mentality. You are not structuring a world that is predominantly manual with 30-40% automation. The objective is to make a completely new procedure, in a world that is predominantly automated and intended for 20% manual exemptions.

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Augmented intelligence is growing as an approach to artificial intelligence, in a way that helps humans complete tasks faster, rather than being replaced by machines entirely. 

In an IBM report called “AI in 2020: From Experimentation to Adoption,” 45% of respondents from large companies said they have adopted AI, while 29% of small and medium-sized businesses said they did the same. 

All of these companies are still in the early days of AI adoption, and are looking for ways to infuse it to bolster their workforce.  

Ginni Rometty, the former CEO of IBM, said in a talk at the World Economic Forum that augmented intelligence is the preferable lens through which to look at AI in the future.

“I actually don’t like the word AI because cognitive is much more than AI. And so, AI says replacement of people, it carries some baggage with it and that’s not what we’re talking about,” Rometty said. “By and large we see a world where this is a partnership between man and machine and that this is in fact going to make us better and allows us to do what the human condition is best able to do.” 

Augmented intelligence is a cognitive technology approach to AI adoption that focuses on the assistive role of AI.

“I would explain augmented intelligence as something where you are augmenting a human being to do their tasks a lot better or more efficiently,” said Dinesh Nirmal, the vice president of Data and AI Development at IBM. “You’re not replacing anyone, but you are augmenting the skill set of that individual.”

The choice of the word augmented, which means “to improve,” reinforces the role human intelligence plays when using machine learning and deep learning algorithms to discover relationships and solve problems.

While a sophisticated AI program is certainly capable of making a decision after analyzing patterns in large data sets, that decision is only as good as the data that human beings gave the system to use.

Full automation is a ‘delusion’
“Full automation is a myth,” said Svetlana Sicular, research vice president at Gartner. “There is quite a bit of delusion that everything can be automated. There are certain things that can be automated, but humans have to be in the loop.”

She added that the majority of situations for creating full automation are very expensive and very hard to reach. 

“Once in a while, AI will go berserk for multiple reasons simply because I’m using it at the edge. So if you consider my phone the edge, I might lose the connection or there might be too many people in this area. Like in one instance, my navigation kept telling me on the highway to turn left while there was nowhere to turn left and I was actually thinking, ‘What if I am in an autonomous car?’ ” Sicular said. 

There are tasks that require expertise and decision-making that can only be accomplished by the essential creativity that only humans could bring to the table, according to David Schubmehl, research director of Cognitive/AI Systems at IDC. 

“AI is really an assistant to help you get done with the mundane and mindless tasks more quickly so you can focus on the more challenging creative aspects,” Schubmehl said. 

Autonomous AI is being used across organizations that typically require very repeatable tasks such as customer churn and telecommunications, recommendations in retail and supply chain, Sicular mentioned. 

Since AI adoption is in its early stages, enterprises don’t necessarily know whether adopting AI models would greatly expedite their efficiency. Sicular said that first there must be a lot of analysis as to whether AI is really worth adopting for certain use cases. Create your own user feedback survey

Sicular also said that there are two large trends happening in the world of AI: the industrialization of machine learning and AI to make them better for scaling, and also the democratization of AI to spread the benefits of the technology evenly.

AI’s move to industries has led companies to look for an all-in-one solution. 

“Data scientists are far fewer compared to developers. That’s why there’s a big effort to try to deliver some kinds of AI in a box that could be slightly modified by developers, and another big effort is how to scale it,” Sicular said. 

Up until recently, all machine learning was done manually, which means there were PhDs who could develop their own custom algorithms. And most of those algorithms were not developed and deployed at scale, according to Sicular. 

“You buy a service off the shelf, you go through the crowd, you can get image recognition, speech recognition, forecasting, text analytics. All of this is available without having specialists in your organization or skilled people and so on,” Sicular said. “But the question that’s at the core of augmented intelligence is how this is being adopted and for what purposes.” 

Augmented intelligence can be seen in sales coaching in which inside sales employees are getting advice as they talk to customers. 

In healthcare, AI is used to help doctors or specialists find some of the things that they have missed rather than sift through hundreds of thousands of documents.

AI for IT is called AIOps, which is augmenting the workload of managers and IT workers by helping them detect anomalies or disruptions much faster. 

“A lot of customers are having trouble with the amount of data that’s coming in,” IBM’s Nirmal said. “Let’s say you have IoT data, transaction data, behavioral data, social data and other places, and the question is how do you do data discovery and its classification. At least every enterprise that we are working with, there’s a lot of interest in adopting AI.”

An example of augmenting would be a data engineer finding data that looks like code but was put into the zip code field. The data engineer can then determine whether the data makes sense in its place. If it’s not a zip code for example, but instead a social security number, then the data engineer can go and change it. The machine learning model will then know that this number is not a zip code for next time. 

Another big area of interest is in creating alerts that can detect anomalies and can be used in data centers, according to Nirmal. 

“Customers are always wanting to figure out a problem before it happens. So anomaly detection becomes pretty critical to make sure that you see a lot of alerts for our data coming or logs coming in,” Nirmal said. “There are some tasks such as fraud detection in which AI tends to generate a lot of false alerts and humans with deep vertical knowledge need to then oversee the process.”

Augmented intelligence can also refer to tools such as AI transcriptions from meetings or add-ons to PowerPoint that make recommendations on how to improve the slides as one goes  through them. 

Developers also have access to tools that use AI to create more efficient code reviews to speed up the SDLC. For example, DeepCode shows errors in code based on troves of similar scenarios that occured before, and then provides context on how to fix them. 

“What Grammarly does for written texts, we do the exact same thing for developers,” said Boris Paskalev, the co-founder and CEO of DeepCode.  “There are many areas where I believe that augmented intelligence actually helped developers because now we have a machine learning representation for code. In the DeepCode platform, you can really add any service on top of it because you really have the knowledge of the global development community that you can index in real-time. So we can get the answers in seconds, which is quite exciting, considering these capabilities did not exist just a couple of years ago.”

All in all, companies are growing their investments in AI and it is becoming a fundamental part of every industry’s digital transformation, according to IDC’s Schubmehl. 

“Amazon wouldn’t be where it was without machine learning and AI, because it’s a huge part of its offering,” Schubmehl said. “We’re really in the early days. We’ve finally gotten out of the prototyping phase and we’re actually starting to put real AI and machine learning into production. I think we still have years and years to go before AI is what I would call fully mature.”

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In the conversation around the application and adoption of artificial intelligence (AI) and cognitive technologies, two recurring types of solutions usually come up: AI solutions meant to work in conjunction with people to help them accomplish their tasks better, and AI solutions meant to function entirely independent of human intervention.The sorts of solutions where AI is helping people do their jobs better is usually referrred to as “augmented intelligence” solutions while those meant to operate independently as “autonomous” solutions. However, increasingly we’ve been seeing reference to the term “assisted intelligence” solutions that attempt to split the hair between different types of AI solutions that are helping people do their jobs. Is the term “assisted intelligence” meaningful and is there a way to provide a clear delineation between “augmented” and “assisted” types of intelligence solutions?

The folks from PwC have been most visible in promoting the different definitions of augmented vs. assisted intelligence. From their perspective, they see a continuum of human-machine intelligence interaction ranging from situations where machines are basically repeating many of the tasks humans are already doing (assisted) to enabling humans to do more than they are currently capable of doing (augmented) to fully accomplishing tasks on their own without human intervention (autonomous). Others are defining the assisted – augmented – autonomous continuum as being one of control and decision-making. From this perspective, in assisted intelligence approaches, machines might be doing the action but humans are making the decisions, while with augmented intelligence, machines are doing the action but there’s collaborative human-machine decision-making, and in autonomous systems machines are making both the actions and decisions. Another group defines things even more simply: assisted intelligence improves what people and organizations are already doing, augmented intelligence enables organizations and people to do things they couldn’t otherwise do, and autonomous intelligence systems act on their own.

While these definitions make some sense, the challenge is applying these terms to the various real-world situations in which they might be used. For example, it’s fairly clear that a Level 5 Autonomous vehicle is exhibiting truly autonomous behavior, especially when there’s no control of the vehicle even possible. But what about Level 2 or Level 3 vehicles? There’s clearly some AI at work here keeping the vehicle in lane and managing various speed and navigation changes. Is this augmented or assisted? I suppose since the system is not providing any capabilities that a human can’t otherwise do, you could call this simply assisted. However, that means in autonomous vehicle situations there is no augmented intelligence role, from that definition.

In other situations, it gets more complex. Are machine-language powered solutions for online recommendation systems augmented or assisted or autonomous? I suppose people could be manually providing recommendations for products, but that’s hardly possible in the context of millions of customers and tons of website traffic. So are these AI solutions augmented or assisted or perhaps they’re autonomous? We tend to think of collaborative robots (cobots) as augmented intelligence because they’re giving humans skills and capabilities they don’t already have. But if they’re just being used to assemble widgets or move things from place to place are they really augmenting anything, or are they just assisting? As we can see the assisted vs. augmented vs. autonomous difference is sometimes entirely from the perspective of not what the AI-enabled system can do, but what it’s actually doing at that time.

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The advent of machine learning witnessed a remarkable rise in the number of speech synthesis projects. Google’s Wavenet paper is one such example that catalysed the whole domain.
Here are the top advancements in speech synthesis that have been boosted with the introduction of deep learning:

Real-Time Voice Cloning

This model was open sourced back in June 2019 as an implementation of the paper Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis.
This service is being offered by Resemble.ai. With this product, one can clone any voice and create dynamic, iterable, and unique voice content.
Users input a short voice sample and the model — trained only during playback time — can immediately deliver text-to-speech utterances in the style of the sampled voice.
Listen To Audiobooks In Your Own Voice

Bengaluru’s Deepsync offers an Augmented Intelligence that learns the way you speak. That is correct — it creates a digital model of the user’s voice and learns hundreds of features including the accent to the way you subtly express oneself. It does this by using advanced forms of deep learning.
Once the voice is synced with Deepsync, the user can record content for the 80–90% of their entire work.

Guessing Face From Speech

Researchers at MIT developed an algorithm Speech2Face that can listen to a voice and guess the face of the speaker with decent accuracy.

During training, the model learns voice to face correlations that allow it to produce images that capture the age, gender and ethnicity of the speakers. The face is generated in a self-supervised manner, by utilising the natural co-occurrence of faces and speech in internet videos, without the need to model attributes explicitly.
The applications of which can be of a wide range- from identifying the speaker in remote locations to giving a voice to those with speech impediments by reverse engineering their facial features. 
Facebook’s MelNet Clones Bill Gates’ Voice

“A cramp is no small danger on a swim,” Bill Gates cautions randomly. “Write a fond note to the friend you cherish,” he advises in a few audio clips released by Facebook AI. However, each voice clip has been generated by a machine learning system named MelNet, designed and created by engineers at Facebook.
MelNet, the model developed by the AI researchers at Facebook, combined a highly expressive autoregressive model with a multiscale modelling scheme to generate high-resolution spectrograms that has a realistic structure on both local and global scales.
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The applications of MelNet cover a diverse set of tasks, including unconditional speech generation, music generation, and text-to-speech synthesis.
Human-Like Speech With Amazon Polly

Amazon’s Text-to-Speech (TTS) service, Polly, uses advanced deep learning technologies to synthesise speech that sounds like a human voice.
Amazon Polly offers Neural Text-to-Speech (NTTS) voices, where one can select the ideal voice and build speech-enabled applications that suit for different regions.
“Amazon Polly voices are not just high in quality, but are as good as natural human speech for teaching a language,” said Severin Hacker of Duolingo, world’s most popular language-learning platform.
Text2Speech With GANs

GAN-TTS is a Generative Adversarial Network that has been used to generate speech from text. The results have shown high-fidelity in speech synthesis. The model’s feed-forward generator is a convolutional neural network that is coupled with an ensemble of multiple discriminators which evaluate the generated (and real) audio based on multi-frequency random windows.

GANs, with their parallelisable traits, make for a much better option for generating audio from the text than WaveNet because it largely depends on the sequential generation of one audio sample at a time, which is undesirable for present-day applications. 
Many speech syntheses deep learning techniques use variants of other fundamental models like RNNs or CNNs. Currently, even GANs are being used to generate audio. These techniques have the potential to revolutionise products ranging from helping the visually impaired to automated music generation, from media editing to customer service.

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A person living with a chronic condition like diabetes often needs a bit of help when it comes to following doctor’s orders in between office visits. Communicating with a chatbot on a personal device can help track and log data on exercise, medications, and general wellbeing.

The challenge is that often the context might drive the conversation into a totally different direction. The robot, as smart as it is, won’t always have the right insight or know the right next question to ask.

That’s why we believe in augmented intelligence: machine learning backed by a medical professional. Allowing bots to work effectively alongside humans provides a highly contextual response when a patient has a specific line of questioning. At the same time, it provides a richer machine learning opportunity to train the bot and fill in gaps in its knowledge.

The problem worth addressing is the very limited time a patient has to see a doctor, coupled with the fact that people have a hard time following a care plan. Even if you have gone through the “social contract” of agreeing with your doctor to do something like exercise or take medication, the actual chances of you doing it are not very high, to begin with, and they become lower as time goes by.

Robots without a human involved could work in health if all of the questions are going to be transactional, such as asking about benefits or deductibles. But it quickly becomes harder because patients tend to want more information or to talk about things they are worried about.

The augmented intelligence approach makes it easier for people to follow their care plans and even be able to link back with their practitioners and make changes on the go, rather than wait three months until the next appointment.

The ability to use bots is the ability to get care 24/7.  Furthermore, bots and technology can provide support for the in-between visits, the in-the-moment type problems that happen on a daily basis – especially for those in chronic conditions.

One big lesson learned about digital technology from the financial services industry is that being efficient is more important than offering a beautiful experience. You want the machines to be smart about answering the questions and also about asking the follow-up questions. And you’ve got to be sensitive to when can you actually triage from the system to a human without causing frustration.

Digital health technology requires context, connection, and continuity to be a success, and that’s what chatbots supply. They’re always awake. And people with chronic conditions often need an answer immediately. The only way that gets practised today is with the person driving to the ER, which of course is an incredibly costly thing.

When people know they can access a 24/7 response, like Amazon and Netflix, they do. And robots are aware of what’s happening with that consumer at any point in time, including location and the context of the situation while being able to triage back to an expert human being.

Is a healthcare consumer ready to now step into the ER and be managed by a machine instead of a human? No. But that same person who just wants to understand if their blood pressure reading is okay wants immediate feedback. A lot of people dealing with chronic conditions are ready to use something more efficient.

Bots are going to play a hugely significant role in the future of how healthcare is delivered. We come up with new conditions and complexities every day, and it’s getting harder for doctors to read and keep up with new research. Machines are really good at learning, so over time, the ability to access knowledge and define situations in a much more granular form will be driven much more by machines.

As health care becomes more focused on outcomes, being able to share risks and rewards with consumers is going to be important as well. Already we’re seeing life insurance companies that are starting to lower the premiums if they can see that people remain active and healthy. The more sophisticated the model becomes, the more technology will be a key part of being able to participate successfully.

We want people to be empowered to handle their health better. And empowered should mean visiting their doctor and having a meaningful conversation with a physician. Using bots can deepen this interaction because it allows us to collect more information, really understand our health habits, and closely examine how our habits actually impact our health. Doctors are there to be our specialist and help us make some of the right choices.

I think consumers are feeling more ready than ever for healthcare to jump a couple of generations ahead because people are so used to technology in other segments of their lives. We will see a lot of momentum in the next three to five years, and the tipping point of mass adoption is probably about five years away. It’s not a question of if, it’s a question of when.

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