Imagine that data could be shared seamlessly with partners, governments, and other organisations, without breaking any data protection law, to facilitate innovation. How will it be possible to use closely guarded customer data while still maintaining the highest privacy and safety standards? Is it possible to monetise data without compromising the sensitivity of the information (or data)? The following write-up spills it all.

Data is the fuel for the rapidly progressing Artificial Intelligence (AI) industry -as it is for almost all other industries. Digitisation, interconnection of the network channels, and IoT generate mountainous volumes of data at an unimagined and unprecedented scale. IDC predicts that more than 175 zettabytes of data will be available by 2025, growing at an exponential rate. Thus, unbelievable data would be available, and accessible to all. However, there are still a huge number of AI innovations and projects that do not reach the stage of viability, owing to an insufficient amount of data.

The inaccessibility of authentic data for innovation and AI

Accurate data collection and processing is extremely straining in terms of expenses and time. While certain sectors like financial services, telecom, healthcare, internet companies, retail, etc, are in direct contact with the customer data, this access is restricted only to these touchpoints.

Then there is another category of data that is fragmented and siloed. Stringent regulations like GDPR watch over the sharing of data that can be processed only with user consent, strictly for lawful purposes. Data security measures are another reason that restricts data access over a larger scale. The data related to Research and Development requires regular hypothesis testing. This is, thus, another challenge pertaining to the presence of any real-time data from the field.

While AI projects require deep learning and innovation are constantly ravenous for large volumes of data, both structured and unstructured, to train models. Scarcity and huge expenses for labelled training data make it difficult to make it available for AI.

These challenges decelerate authentic data monetisation and realisation of benefits (from innovation and business). This is a major reason for many great projects to never even see the light of the day. While alternatives like data masking, anonymisation, and obfuscation ensure data security and privacy to abundant data, synthetic data is a preferred choice when data is scarce.

Synthesising data

Data generated as a result of using computer algorithms and simulations to the real-time data is synthetic data. Digitally synthesising data reflects the real-world either statistically or mathematically. Simply put, the data is generated by reproducing the statistical properties and patterns of the existing real-time datasets. This is done by modelling the probability distribution of these datasets and sampling them out. Essentially, the algorithm creates a new dataset with the same characteristics as the original data.

Although this synthesised data lead to the same answers, is it almost impossible that the original data can be ever reconstructed from either the algorithm or this synthesised data. Thus, synthetic data is almost as potent as the original data with equal predictive power. It further carries no baggage of privacy concerns or restricted usage of any kind.

Synthetic data is being increasingly employed for a wider range of applications. For instance, Syntegra is using its synthetic data generator to create and validate an anonymous replica of NIH’s database. This database has a record of more than 2.7 million individuals that have been screened for COVID-19 and more than 413,000 patients that have tested positive. This synthetic data set duplicates the real-time data quite precisely. Due to its anonymous nature, the data can be conveniently shared and used by researchers and medical professionals worldwide. A remarkable step to accelerate the progress in research for COVID-19 treatment and vaccines.

The AI team at Amazon uses synthetic data on Alexa for training its NLU system (National Language Understanding). Consequently, new versions of Alexa in three new languages have come out: Hindi, Brazilian Portuguese, and US Spanish. This invalidates any further use of large customer interaction data. Synthetic data is also being used by Waymo, a Google company, to train its autonomous vehicles. Synthetic data technology is extremely useful for American Express to enhance its fraud detection capabilities.

Synthetic data could, therefore, fill in the gaps that are hindering the evolution of AI technology. These are proving increasingly helpful in creating inexpensive, yet accurate, AI models. According to MIT Technology Review, Synthetic data for AI is among the top 10 breakthrough technologies in 2022. Analyst firm Gartner predicts: “By 2024, 60% of the data used to develop AI and analytics projects will be synthetically generated. The fact is you won’t be able to build high-quality, high-value AI models without synthetic data.”

Potential challenges

With compelling benefits, authentic and accurate synthetic data generation requires truly advanced knowledge and specialised skill sets. Further, the required sophisticated frameworks should be put up to enable its validation and alignment with the objective.

It is critical that the synthetic data generated does not relate to or expose the original data set in any way, while it should match the important patterns in the original. Failing would result in either overlooking potentially larger opportunities or generating inaccurate insights for any subsequent efforts to model the data.

For the AI models that have been trained on synthetic data generated by simply copying the original one, there is always a risk that inherent historical biases might creep in. Complex adjustments are, therefore, necessary for a fairer and more representative synthetic data set. Hard, yet achievable.

For the synthetic data generated that has been optimised on a predetermined abstract of fairness, the resulting dataset accurately reflects the original one while still maintaining inherent fairness. So, no bias mitigation strategies are needed, with no compromise on predictive accuracy.

The certainty of synthetic data in AI’s future

“There is a risk of false, early-stage perceptions surrounding the use of synthetic data in some circles. This is most likely due to the naming of the term itself, as anything ‘synthetic’ might naturally be thought of as plasticized, non-organic, or in some way fake. But, of course, there should be nothing more natural than machine learning tuition being driven by machine intelligence. Properly generated, managed, maintained, and secured, synthetic data’s level of bias handling, safety, privacy, and cadence represent a significant accelerator and enabler for the AI capabilities of tomorrow”, Nelson Petracek, CTO, TIBCO Software.

Already being used in healthcare (for training the machines to monitor a patient’s post-op recovery), security and surveillance (for detecting a suspicious object or behavioural pattern), and delivery drones, synthetic data is progressively making an accelerated advancement in its evolution.

Synthetic data, surely, is synthetic in origin, but it has real-world DNA to it. Its validation and application are unbelievably tangible, pragmatic, and multifarious. Nevertheless, it is, in fact, a reality we all exist in.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post The Shift In The Paradigm To Synthesise Data For A Data-Driven AI Industry appeared first on OpenBusinessCouncil Directory.

The constantly shifting paradigms of humanitarian actions, owing to the increased complexity and range of needs, has resulted in a steep demand for innovation in the area. Technological advancements, like Big Data analytics and AI, have proved effective and efficient for humanitarian applications to date. However, like any other innovation, it has also introduced new challenges and risks, making the end-user vulnerable to its repercussions. Redesigning AI seems to be the most plausible solution to accommodate the dynamics of various parameters within the sector. 

The world has been in constant strife to meet people’s demands while improving the efficiency in the humanitarian sector since the very beginning. Improvements and innovations in technology have led to the augmented use of artificial intelligence (AI) systems since the past few decades to satiate this hunger. However, the application has always been debated and ignited controversy, owing to its ethical and human rights-related implications.

“The instrumental conception of technology conditions every attempt to bring man into the right relation to technology. Everything depends on our manipulating technology in the proper manner as a means. We will, as we say, “get” technology “spiritually in hand.” We will master it. The will to mastery becomes all the more urgent the more technology threatens to slip from human control. But suppose now that technology was no mere means, how would it stand with the will to master it?”

Heidegger, Martin. “The question concerning technology (W. Lovitt, Trans.) The question concerning technology: and other essays (pp. 3-35).” (Heidegger 1977).

In 2013, the United Nations Office for the Coordination of Humanitarian Affairs officially proposed the recognition of information during crises as a basic humanitarian need. This led to a series of digital transformation and innovation initiatives in the humanitarian sector.

Various AI applications like chatbots, biometrics, satellite imaging, and Big Data analysis became an essential and inevitable part of the wider phenomenon. Critical algorithms were developed to focus primarily on assisting human intelligence for betterment. However, the phenomenon drew greater power asymmetries with time. What began as the “AI for social good” movement gradually started turning to “unethical innovation”.

Chatbots, for instance, has been around for quite a few years now, becoming a prevalent part of our digital culture now. Even though these bots don’t intend to take over the world (like in Transformers or Matrix) or destroy humanity just yet, there have been instances of less-than-professional behaviour shown by them in the past. Microsoft’s Teenage chatbot Tay and its successor Zo, and Nabla (a text generator at the Parisian healthcare facility) are just a few examples of AI going wrong.

Chatbots (and other AI) are based on simple algorithms and are programmed manually. Their actions are explicitly based on the mappings chalked out by the creators. Natural language, on the other hand, is a complex thing with slang, misspellings, humour, and intonations. How can it be expected that interactions involving sarcasm and intonations (that are sometimes even beyond human comprehension) could be interpreted appropriately by a machine? The latest advancements based on humanitarian innovation are the prime focus area for researchers.

Redesigning AI for Humanitarian Innovation

Though there is no specific definition of humanitarian innovation, according to HIF-ALNAP (by Obrecht and Warner, 2016)

“Humanitarian innovation is an iterative process that identifies, adjusts, and diffuses ideas for improving humanitarian action”. 

This leads to consolidated learning, bringing a measurable and relative improvement in the effectiveness, efficiency, and quality of the innovative approaches over the conventional ones. Further, it also improves the scale of adoption to improve humanitarian performance. In the end, this all leads to more exploration and research.

Currently, organisations that are involved in humanitarian endeavours collect, store, and share huge amounts of personal information. Challenges, however, arise when this data is mistreated or misused. There are a variety of guidelines for the promotion of ethical and fair use of AI across different sectors. These recommend the designing, development, and deployment of the latest innovation and technology.

Further, AI reduces communication to its barest instrumental forms. This widens the gap between the humanitarian efforts and the society itself. The disconnects compound further when data experiments with untested technology. This actually degrades the value of data. In other words, it is the reproduction of the colonial powers of AI.

Sensitive data, especially from the vulnerable demographics (such as the Internationally Displaced Persons, refugees, mental health patients, and senior citizens), needs to be handled in a sophisticated manner to ensure its privacy and security. AI redesign, therefore, is aimed to broaden and deepen the interdisciplinary efforts to enhance the innovation on humanitarian assistance.

Redefining the Future of Humanity

Though AI is paced to make huge advances to revolutionise medicine, transport, employment, and markets, it is ready to take a leap and reshape the fabric of society (and its citizens). On the other hand, its perils, owing to increased automation and misinformation, are threatening the very society with bias and surveillance.

“Whenever I hear people saying AI is going to hurt people in the future I think, yeah, technology can generally always be used for good and bad and you need to be careful about how you build it … if you’re arguing against AI then you’re arguing against safer cars that aren’t going to have accidents, and you’re arguing against being able to better diagnose people when they’re sick” —Mark Zuckerberg

Redirecting (and arguably redesigning) AI is one step closer to a smoother and seamless work, democracy, and justice for humanitarian efforts in society. However, the question remains- how. Time will reply that soon.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post A Seamless And Smoother Future Of Humanitarian Efforts With Redesigned AI appeared first on OpenBusinessCouncil Directory.

As 2021 draws to a close, Thematic Analyst Robert Penman offers his view on these trends:

Further XAI rollout expected while companies acquire synthetic data training start-ups to poach talent

“2022 will see the further rollout of XAI, enabling companies to identify potential discrimination in their systems’ algorithms. Companies must correct their models to mitigate bias in data. Organizations that drag their feet will face increasing scrutiny as AI continues to permeate our society, and people demand greater transparency. For example, in the Netherlands, the government’s use of AI to identify welfare fraud was found to violate European human rights.

“Reducing human bias present in training datasets is a huge challenge in XAI implementation. Even tech giant Amazon had to scrap its in-development hiring tool because it was claimed to be biased against women.

“Further, companies will be desperate to improve their XAI capabilities—the potential to avoid a PR disaster is reason enough. GlobalData expects to see acquisitions of start-ups specializing in synthetic data training in an attempt to poach talent, demonstrated by Meta’s acquisition of AI.Reverie in October 2021.”

AI will need to become practical and unobtrusive for smooth integration into software

“After years of AI hype, we are now in the era of practical AI. It will be integrated into the software and will play its role unobtrusively and autonomously. Look no further than Amazon Go for an example of automated AI in action.

“There will be fewer unmet promises of AI revolutionizing life as we know it. Instead, companies will focus on creating actual AI use cases.”

Big tech’s advantage in AI will only widen as small firms can do more with less data

“Data and computing power are essential for AI development, and big tech companies have both in abundance. There are vast numbers of AI start-ups, but their future often lies in being acquired by big tech.

“However, the future of AI may be less dominated by big data. Doing more with less offers smaller companies a glimmer of hope. This is being made possible by innovative methods that enable algorithms to be trained using limited information.”

Information based on GlobalData’s report:  Tech, Media, & Telecom Predictions 2022 – Thematic Research

About GlobalData

4,000 of the world’s largest companies, including over 70% of FTSE 100 and 60% of Fortune 100 companies, make more timely and better business decisions thanks to GlobalData’s unique data, expert analysis, and innovative solutions, all in one platform. GlobalData’s mission is to help our clients decode the future to be more successful and innovative across a range of industries, including the healthcare, consumer, retail, financial, technology, and professional services sectors. PR14710

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post 2022 Predictions: Further Roll Out Of ‘Explainable AI’, And More Practical Applications Of AI Lie Ahead predicts GlobalData appeared first on OpenBusinessCouncil Directory.

Artificial Intelligence is a super hot topic at the moment. But most people don’t know how to fully grasp it. You’re probably interacting with AI more than you realize. It decides if your incoming email is spam, if you’re qualified for a loan, and whether or not that person on a dating website is a good match for you. Even down to the music suggestions you are listening to on Spotify and Siri directing you to the nearest restaurant. And all those things are not actually magic and in the ‘future is now’ cliché kind of way you are on the receiving end of this technology every single day. So, the future isn’t now. It’s actually yesterday. 

Why are AI and Machine Learning So Important? 

Training an algorithm to predict future outcomes, using a principal component model to learn about your clients’ personalities, analyzing text to extract sentiments automatically on your product and grouping 650,000 lines of data to cluster clients into different segments with machine learning – all sounded like rocket science to millions of entrepreneurs some 15 years ago. Now lots of ecommerce business owners do it as easily as they use Excel or Illustrator. So, if you want your business to succeed right here, right now, make sure to harness the potential of AI and machine learning. 

• If you’re still hesitant, follow the link to learn more about machine learning services and discover new opportunities for your business.

In five years or so, ‘proficient in machine learning’ will be a must-have on any manager’s CV. But for now it is still a rare skill, which you would be well-advised to develop to take your startup or existing business to the next level. Being knowledgeable about AI and machine learning will give you a strong competitive advantage over your rivals and help you employ the latest techniques wherewith you’ll be able to boost your entrepreneurial venture even more. 

You have probably heard about that, but the digital skills gap is growing exponentially, and you’re going to be required to learn an ever-growing number of new fundamental skills if you want to stay relevant and on top of your game. Master the fundamentals and you can master the rest! 

Business Benefits

AI and machine learning specifically are fundamentally going to change your role not only as a manager or marketer, but also business owner or CEO. This will allow you to be much faster and make use of much bigger data sets, including that of your competitors. You’ll be able to make predictions, correlations, and understand which of your efforts give you the highest ROI. AI will free you from all the repetitive monkey work and allow you to hone in on important aspects like strategizing or team dynamics. 

So, what are the most important questions that AI can answer for you right now? Well, the array is truly endless. Among of the most popular ones are:

• If someone discovers your product or service today, how likely is that person to actually sign up and purchase it? 
• Which of your visitors are most likely to buy your goods or avail themselves of your services? 
• How do your customers feel about your products and services provided?
• How much will your customer spend over their lifetime? 
• Can you discover the psychographics (the interests, habits, opinions, attitudes) of your customers using machine learning? 

The list can be continued, of course. But the most important thing about it is that the answer is ‘Yes’ to all of the aforementioned questions. There is so much more you can apply AI and ML to. And there are lots of real-life examples illustrating the applicability and effectiveness of techniques in question. Thus, we all know that Donald Trump won the presidential elections. But did you know that it was AI that helped him target different personality types with different messages? We’ll bet you had no idea. Amazon also uses AI to create its recommendation engine and show you your next ‘holy-moly-I-need-this-product’ recommendation. Another big company, ASOS, knows exactly how much you’re going to spend with them over the course of your customer lifetime thanks to ubiquitous AI technologies. OTTO, in their turn, utilizes the AI algorithm designed for the CERN laboratory to cut down on stock and predict what people will buy in the next month. And that’s with almost 90% accuracy.  

Now that you know so much about the business benefits of AI and machine learning, you’ll definitely want to give it a shot! It’s the surest way to future-proof your business and ensure your venture will stay relevant and competitive in the market.

Hernaldo Turrillo

Hernaldo Turrillo is a writer and author specialised in innovation, AI, DLT, SMEs, trading, investing and new trends in technology and business. He has been working for ztudium group since 2017. He is the editor of openbusinesscouncil.org, tradersdna.com, hedgethink.com, and writes regularly for intelligenthq.com, socialmediacouncil.eu. Hernaldo was born in Spain and finally settled in London, United Kingdom, after a few years of personal growth. Hernaldo finished his Journalism bachelor degree in the University of Seville, Spain, and began working as reporter in the newspaper, Europa Sur, writing about Politics and Society. He also worked as community manager and marketing advisor in Los Barrios, Spain. Innovation, technology, politics and economy are his main interests, with special focus on new trends and ethical projects. He enjoys finding himself getting lost in words, explaining what he understands from the world and helping others. Besides a journalist, he is also a thinker and proactive in digital transformation strategies. Knowledge and ideas have no limits.

The post AI and ML as an Inexhaustible Source of Business Opportunities appeared first on OpenBusinessCouncil Directory.