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.

Being a hiring manager requires you to take care of a number of things simultaneously, ranging from looking for quality candidates and vetting talent to scheduling interviews. In other words, you have plenty of things to take care of at all times. There are 5 recruiting tools that every hiring manager should use .

5 Recruiting Tools That Every Hiring Manager Should Use

In such a fast-paced environment, it is easy to start falling behind. Luckily, you can prevent that from happening with the help of a wide range of tools, such as applicant tracking software, artificial intelligence, and paid advertisements.

Would you like to learn more about how to make the process of finding the right candidates as easy and efficient as possible? If so, keep reading! Below, you will find a list of five tools that organized and effective hiring managers use on a daily basis.

5 Recruiting Tools That Every Hiring Manager Should Use

Applicant Tracking Software

Have you ever thought about using applicant tracking software? It is a piece of software that can create a central database with the applicants’ information, as well as help you follow each applicant through the entirety of the hiring process.

By using applicant tracking software, you can build talent pipelines of highly qualified candidates, which will allow you to decrease both the average amount of time needed to fill a position and the cost per hire.

While the best applicant tracking software comes with substantial subscription fees, the increase in recruiter productivity will definitely compensate for the said fees. In addition, you could always use a website like CouponNinja to look for discount codes.

Artificial Intelligence

If you are sick of dealing with repetitive and high-volume tasks, such as choosing the right candidates from a large applicant pool and proofreading job descriptions, artificial intelligence is something that you should look into.

You can use recruiting tools powered by artificial intelligence to do many different things, including auto-screening candidates and analyzing job descriptions so as to eliminate potentially biased language. It all depends on what you want to accomplish.

In case you are not sure about using artificial intelligence, think about its benefits! It could help you improve the quality of each hire and save time by automating tedious tasks, as well as reduce cost per screen.

Paid Advertisements

One of the simplest ways to find qualified candidates is to use paid advertisements. Ideally, such advertisements should target potential employees based on their location and whether they have recently visited a job board.

The paid advertisement can be a simple and aesthetically pleasing banner with a photo of an employee of the organization that you work for, as well as the title and the salary range of the position that you are looking to fill.

If you can afford it, you can have that banner show up on a wide range of platforms, ranging from search engines to social media platforms like Facebook and Twitter. On the other hand, if you cannot afford to spend much on a recruitment campaign, you can limit yourself to just one platform and see how it turns out.

Candidate Scheduling Software

Having to schedule a large number of interviews for a wide range of positions on a regular basis can be exhausting, particularly if you have to do it all by hand. Fortunately, you can simplify the entire process with candidate scheduling software.

As the name suggests, candidate scheduling software can help you to automate the process of scheduling interviews with candidates. When it comes to candidates, it can give them an option to book interview slots without any human interaction.

When it comes to recruiters, it can sync with their schedules and book conference rooms for the duration of their interviews. Consequently, it can be particularly useful for large companies that need to conduct tens of interviews per day.

Social Media Platforms

Last but not least, you should think about using a few social media platforms to look for and recruit candidates. The go-to social media platform of the majority of recruiters is LinkedIn, but you can always use other platforms, such as Twitter and Facebook.

Doing that will enable you to reach passive candidates, gather referrals, target desired candidates, and save money. On top of that, it will give you an excellent opportunity to showcase the organizational culture of the company that you work at!

In Conclusion

To sum up, speeding up the process of finding qualified candidates is not as difficult as it might seem. You just need to start making use of the latest recruiting tools, such as applicant tracking software and candidate scheduling software. It might be difficult for you to use a specific tool the first time you open it up, but with enough time and effort, you should be able to figure it out in no time.

However, if you do end up having trouble with some tool, you can always look for a tutorial online. Alternatively, contacting a customer service representative of the company that is responsible for that tool is always a viable option.

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 5 Recruiting Tools That Every Hiring Manager Should Use appeared first on OpenBusinessCouncil Directory.

If the experts are to be believed, agricultural data holds the key to the future of the food supply chain. In this article, we share how agri data is being used today to support decision making, the potential applications of big data analysis in the future, and how it will revolutionise the agricultural supply chain.

How data analytics is being used in the agriculture sector today

The agriculture sector has been quick to embrace the various data-driven solutions that are available today. Here are some of the best applications of big data analysis in the world of agri data today.

Increasing productivity

In an effort to increase yield, farmers and agribusinesses are now leveraging data science and innovation. With the help of soil sensors, weather forecasts and GPS equipped tractors it’s not possible to optimise the operations and discover opportunities to make the most of the available resources.

Reducing wastage and increasing profits

In order to remain profitable, many agribusinesses have developed data analytics strategies that provide them with higher visibility of the supply chain, which in turn lets them make evidence-based decisions regarding pricing.

Getting better at supply chain management

With the help of data analytics, it’s now easier than ever for farmers to know where their products are in the supply chain. By having this information, farmers find it easier to communicate crucial information to the key stakeholders in the supply chain such as the retailers and distributors.

Gain a better understanding of unpredictable environmental factors

There are several unpredictable environmental factors that affect the agribusiness supply chain such as weather and insect behaviours. While it was mostly guesswork to figure out these factors in the past, farmers are now better prepared for such challenges and make use of real-time predictive analytics to make important decisions.

How data will shape the future of the agricultural supply chain

While there are several avenues of research and countless undiscovered opportunities, here are some of the most crucial applications of data analytics in the agricultural sector that are expected to transform supply chain management.

Using machine learning to improve crop management

Gone are the days when farmers had to rely on fellow workers and farming experts for advice on which crops to grow during which time of the year. In the years to come, farmers will be able to completely avoid failed growing seasons and make use of bioprospecting for the identification of plants they can grow and breed together in order to get higher yields from their fields.

Eliminate inefficiencies in the supply chain

Being able to track the movement of food products from the fields to local markets can help farmers and agribusinesses identify inefficiencies in their respective supply chains. This will not only help them stay one step ahead of the market demands but also deliver their goods in a more cost-effective and timely manner.

Providing insights for making risk assessment

With the help of satellite imagery drones and artificial intelligence, it will become easier for farmers and agribusinesses to make risk assessments based on the insights gained from big data analysis.

Predict drought with the help of artificial intelligence

Not only can artificial intelligence help farmers reduce their water and energy costs significantly, but also provide them with the right kind of insights so that they can better assess the possibility of drought and make preparations for it well in advance.

Make urban farming more accessible

Thanks to big data analysis and IoT, more and more farmers will be able to adopt urban farming and maximise their yield and profits at the same time, while also feeding the growing population. According to some estimates, it will help them produce up to 180 million metric tons of raw food products annually, which will be a major boost for the agriculture sector.

Make livestock management easier

With the help of advanced sensors, livestock farmers will soon become better at monitoring the fertility and milk production capacity of their livestock. Apart from this, it will also become easier for them to track and prevent illnesses from spreading in a herd of cattle.

Final thoughts

Considering that agriculture has always benefited from innovation and technological advancements in the past, it won’t be wrong to say that big data analytics would ensure a bright future for agribusinesses. As the global population continues to grow and food supplies remain impacted by climate change, everyone in the agriculture supply chain will be able to better utilise the available technology and overcome the challenges that the future holds for the agriculture sector.

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 Is Agricultural Data The Future For The Food Supply Chain? appeared first on OpenBusinessCouncil Directory.