Archil Cheishvili is CEO and co-founder at GenesisAI, a global network of artificial intelligence products and services.

With the latest advancements in artificial intelligence (AI), achieving human-like intelligence is gradually transitioning to the realm of possibility. And with disruptors like the Covid-19 pandemic ravaging the global economy, the race to achieve artificial general intelligence may have sped up significantly.

I would like to share some insights on the challenges and opportunities intertwined with the path to achieving AGI and the future ahead.

What is artificial general intelligence?

Artificial intelligence can be broadly categorized into three main types: artificial narrow intelligence (ANI), artificial general intelligence (AGI) and artificial superintelligence (ASI). Amongst these, AGI positions artificial intelligence at par with human capabilities. As a result, AGI systems can think, comprehend, learn and apply their intelligence to solve problems much like humans would for a given situation.

The anthropomorphic capabilities that convert artificial intelligence into artificial general intelligence include:

• Sensory perception.

This Company Raised $100 Million To Bring Gene Therapy To The MassesFront Of The Pack Raises $10 Million To Optimize Doggy NutritionInnovationRx: Crispr 3.0; Plus, Pandemic Drug Overdoses Surge

• Fine motor skills.

• Natural language processing and understanding.

• Navigation.

• Problem-solving.

• Social and emotional engagement.

• Creativity.

In simpler words, if AGI is achieved, machines would be capable of understanding the world at the same capacity as any human being. And based on these external inputs, they can discover solutions to an ongoing problem.

Challenges In The Way Of Artificial General Intelligence

While AGI may not have been realized so far, it promises a world of fruitful possibilities. However, it is currently plagued with serious roadblocks, which are present in the form of the following:

• The lack of a working protocol to help with artificial intelligence or machine learning networking is problematic. This deficiency coerces systems to work as standalone models in a closed environment. And such a mode of operation is a stark contrast from the convoluted and highly social “human experience.”

• Communication gaps come in the way of seamless data sharing and the inter-learning of machine learning models, which reduces universality. 

• The absence of an artificial intelligence network also hinders the overall development of a common goal.

• Organizational executives are in the dark on how to integrate AI with their business operations to drive specific results.

• The lack of direction, complemented by the fact that companies cannot afford to hire a dedicated team of AI experts, makes the implementation of AI platforms costly.

• AI developers and companies often experience issues while selling their code and services.

How Can AGI Be Created?

There are three important goals that should be achieved in order to potentially create AGI.

1. We must connect companies in need of AI technologies with developers looking for monetization opportunities, which is made possible through an AI marketplace.

2. We should start interconnecting AI services and networks to create data lakes that can power AGI. The interactions between various AI platforms will help develop universal machine learning solutions.

3. We can begin democratizing access to AI technologies and challenging oligopolies to offer technologically advanced solutions for all.

These three goals can be achieved by setting up communication protocols for data and service exchanges, while also making AI more accessible through an end-to-end AI marketplace. The former helps to potentially lay the groundwork for AGI, while the latter connects companies and developers to reduce time to market.

Final Thoughts

The next decade will play a crucial role in accelerating the development of AGI. In fact, experts believe that there is a 25% chance of achieving human-like AI by 2030. Furthermore, advancements in robotic approaches and machine algorithms, paired with the recent data explosion and computing advancements, will serve as a fertile basis for human-level AI platforms. 

Now, it is only a matter of time until AGI becomes a part of the new normal.

The post The Future Of Artificial General Intelligence appeared first on Artificial Intelligence.

Artificial intelligence (AI) is eating the world, one boring, routine task at a time. 

From navigation apps using AI to crunch a bunch of data at a super-fast speed to determine the best and fastest route from A to B, or automatic spam filters and categorizations that make email more manageable, AI is truly ubiquitous.

It was only a matter of time before AI applications in the content management space arose. And when it comes to content ops, the combination of content management and artificial intelligence is a great tool for giving workers back the time they need to perform more complex tasks that still require a human brain.

The state of play – how content management and AI are already impacting Content Ops 

AI excels at ‘understanding’ vast pools of data and automating routine tasks. This is typically orientated towards objectives of improving consumer experiences, saving the time and money invested in routine processes, and even exposing patterns that can uncover new revenue opportunities. 

These are typically seen in a content operations workflow in four areas:

1. Smart Content Analysis

AI can analyze a piece of content to identify its sentiment and overall tone, very quickly. This is important for helping content managers determine whether a piece of content is right for their audience or if it needs tweaking before it will truly engage the intended consumer. IBM Watson, for example, uses AI to automate content categorization, text labeling, sentiment analysis, keyword extraction, and more.

2. Automatic Image Tagging

A picture still tells a thousand words.  Images enhance content increase engagement. Unfortunately, there is almost nothing less engaging for workers than manually tagging image after image for search and SEO purposes. But it is still a supremely important task. And that is what makes it a great job for AI.

AI-powered automated image recognition is now smart enough to tag images in a matter of seconds—letting content workers get back to deeper work instead of routine classification.

3. Scalable Personalisation and Predictions

AI also brings scalability to another important but nearly impossible task for human staff: tracking and making use of individual user behavior. 

AI can automate the process of watching what each user on a website or app is doing simultaneously. Then, it can compile this data to look for patterns that will help it predict, based on past behavior, what each user might want next.

This information can dramatically improve personalization efforts, from serving dynamic content to making product recommendations and more. And improving personalization has never been more important. In the words of the management consulting firm McKinsey, “Personalization will be the prime driver of marketing success within five years.” In fact, they found that leaders in personalization were already able to increase revenue by 5 to 15 percent and improve efficiency on marketing spend by 10 to 30 percent. Achieving that kind of improvement automatically is a massive competitive advantage.

4. Time-Saving Content Creation Assistance

Controversially, AI can also be a big help when it comes to creating content. 

Whilst artificial intelligence still is not great at coming up with original ideas or creating nuanced pieces of content, it is catching up fast. A well-trained AI tool should be able to contribute to straightforward writing projects such as news articles, factual reports, translations, transcriptions, and editing for accuracy. 

At present, in the content creation workflow, AI is basically a tool for improving the ROI on content marketing, which can often be resource-intensive. Simply put, AI can do the legwork when it comes to research and data while human writers can take this material and do the deep work required to create high-value, relevant content for each target customer.

The shape of things to come 

Based on these areas in which content management and artificial intelligence are already coming together to improve content operations, AI may improve marketing even more in the future.

Interactions Between AI Tools

AI interactions already abound in the consumer space.  A voice-activated smart speaker to controlling house lights or audio is an everyday example. Similar interactions are in store for the future of AI-powered tools in the content operations space. It is only a matter of time before AI-enabled content management systems (CMSs) and other content platforms and tools will be able to interact with each other in smart, automatic ways to provide faster functionality and better experiences for consumers and marketers alike.

On-the-Spot SEO Improvements

Taking the idea of sentiment analysis one step further, soon AI-enabled CMSs may be able to identify opportunities for SEO improvements in real-time. This capability would empower marketing professionals to create more effective content in less time, which will outperform competitors and rank better in search engines.

Content Gap Identification

Whilst AI may not create great content on its own, it can spot a lack of it – especially if it has access to huge pools of data on customer behavior preferences.   This can then alert a business to where its content may be lacking (or where competitors’ content may be lacking). 

Both situations are a huge opportunity to fill those “gaps” and capture more traffic. AI is becoming smart enough to flag gaps and make recommendations so businesses can create fresh content that adds value and generates new leads.

Customer Service AutomationsAdvertisementhttps://5e8e67e6d9f34805ab09242966406c65.safeframe.googlesyndication.com/safeframe/1-0-38/html/container.html

Customer service is another expensive yet necessary element of business. Chatbots have already come to the for as a way of reducing both the time and money needed to deliver customer service excellence. 

While many of today’s chatbots can address very simple questions with answers pulled from a knowledge base, the future will see a large percentage of queries – if not the majority – that do not have to be routed back to human agents. After all, it is the instantaneous and round-the-clock support that consumers are truly looking for when interacting with brand chatbots.

Bridging the gap – going headless 

At the heart of this evolution from where AI and content management is today to where it could be tomorrow, is the need to integrate content management systems with an array of new technologies driven by AI.  

In practical terms that means developing headless architectures that will enable content operations teams to explore and exploit everything from automated content analysis to smart content creation. Adopting this strategy will leave a business ready to capitalize on the next wave of AI-based innovation with minimal disruption, driving return on investment.

The post Content management and Artificial Intelligence – the future of ContentOps appeared first on Artificial Intelligence.

When thinking of technology, one cannot go without talking about deep learning. Needless to say, deep learning has become one of the most critical aspects of technology. Gone are the days when organizations alone used to show interest in technologies like AI, deep learning, machine learning, etc. Today, even individuals are inclined towards the very aspect of technology, deep learning in particular. One of the many reasons why deep learning draws all the attention is because of its ability to enable improved data-driven decisions and also improve the accuracy of the predictions made.

In a nutshell, companies are in a position to reap out various financial and operational benefits by virtue of deep learning. With many deep learning innovations proliferating with time, it makes every possible sense to have a clear picture as to how does the future of deep learning looks like. In line with what we have seen over the past few years, this is what we could expect in the coming days as far as deep learning is concerned –

• Despite the fact that deep learning is a little on the slower side when compared to traditional AI and other machine learning algorithms, what one can stay assured of is the fact that it is way more powerful as well as straightforward. It is because of this that fields such as medicine, supply chain, robotics, manufacturing, etc. would see immense usage of deep learning in the days that lie ahead.

• A few years from now, it is very much possible that deep learning development tools, libraries, and languages could become standard components of every software development tool kit. These tool kits with modern capabilities will pave the way for easy design, configuration, and training of new models. With these capabilities, style transformation, auto-tagging, music composition, etc. would be a lot easier to accomplish.

• The need for faster coding is at an all-time high. The future is all set to see the deep learning developers adopting integrated, open, cloud-based development environments that provide access to a wide range of off-the-shelf and pluggable algorithm libraries.

• The prediction that neural architecture search would play a pivotal role in building data sets for the deep learning models still stands strong.

• Global marketers have a positive mindset by virtue of Google’s acquisition of DeepMind Technologies.

• It is highly likely that the deep learning networks would demystify computer memory.

• Yet another point that is worth making a note of is the fact that the automation of deep learning tools would mean that there’s an inherent risk that could develop into something so complex that the average developers will find themselves totally ignorant.

• Deep learning should be able to demonstrate learning from limited training materials and transfer learning between contexts, continuous learning, and adaptive capabilities. Wondering why. Well, just to remain useful.

What everything boils down to is the fact that as a result of the growing popularity of deep learning and with the advancement in technology, by the end of this decade, the deep learning industry will simplify its offerings considerably so that they’re comprehensible and useful to the average developer.

The post THE FUTURE OF DEEP LEARNING appeared first on Artificial Intelligence.

The future of Data Science is Growing with the Advancement of AI and Machine Learning.

Data science in simple words means the study of data. It entails developing methods of recording, storing, and analyzing data to successfully bring out useful information. Data Science put together and make use of several statistical procedures. The procedures cover data modeling, data transformations, machine learning, statistical operations including descriptive and inferential statistics. For all data scientists statistics is the primary asset.

With the biggest innovation of the time, that is a cryptocurrency, the demands for controlling data online have become a crucial challenge. Various techniques are put forward by Data Science to identify a group of people and providing them with the best possible security from fraud activities.

However, the application of data science is not just concerned with one field rather its application disseminated across various sectors.

Major Future Developments in Data Science-

Healthcare sector- The biggest application of Data Science is in healthcare. The accessibility of large datasets of patients can be used to build a Data Science approach to identify the diseases at a very early stage. Healthcare is one of the biggest sectors for providing opportunities for the professional who can use their medical expertise with Data Science and provide immediate help to the suffering patients.

Arms and Weapons- Data Science can help in building various automated solutions to identify any attack at a very early stage. Other than that Data Science can help in constructing automated weapons that will be smart enough to identify when to fire and when not to.

Banking and Finance- Data Science in the Banking and Finance sector can be used in managing the money effectively to invest in the right places based on Data Science predictions for best results.

Other than the above sectors Data Science is also applied in Automobile Industry like self-driving cars, Fixed destination cabs as well as in Power and Energy. Data Science can predict the maximum safest potential and can help in building AI bots that can easily handle enormous power sources.

The implementation of Data Science cannot be ignored as it is already in action in the present stage. When you look for something in Myntra or Flipkart and then you get similar recommendations or similar advertisements for whatever you have searched on the internet is all about Data Science. The whole world is operated by Data Science. For every single search in Google, the process of data science is activated.

The future of data science is growing. According to Cloud Vendor Domo even when a person accounts for the Earth’s entire population, the average person is expected to generate 1.7 megabytes of data per second by the end of 2020.

An overreaching motif today and moving ahead, big data is assured to play an authoritative role in the future. Data will stipulate modern health care, finance, business management, marketing, government, energy, and manufacturing. The scale of big data is truly staggering as it has already entwined itself in the fundamental aspect of business as well as personal life.

The Dominance of AI and Machine Learning in near Future of Data Science-

Like almost all businesses prime concern is tech, there is a high possibility of the growth of data science jobs.

Artificial Intelligence is the most impactful technology among others that data scientists will run up into. Today Ai is already refining the business operations and assures to be a major trend in the near future. The applications of AI in today’s world have driven the adoption of other AI applications such as machine learning, deep learning and this will lead the way as the future of data science. Machine learning is the aptitude of statistical models to develop the capabilities and improve the performance with time in the absence of programmed instructions. This principle can be seen in the chess machine that is developed by Google’s DeepMind unit – the AlphaZero. The AlphaZero improves on its other computerized chess-playing peers in the absence of instructions is an example of how it learns from its movements to reach the most desired outcome.

As a greater number of businesses are merging with AI and data-based technologies at a high rate there is a need for a greater number of data scientists to help guide the initiatives.

Data science is a leviathan pool of multiple data operations that include statistics and machine learning. Machine Learning algorithms are very much dependent on data. Therefore, machine learning is the primary contributor to the future of data science. In particular data science covers the areas like Data Integration, Distributed Architecture, Automating Machine learning, Data Visualisation, Dashboards and BI, Data Engineering, Deployment in production mode, Automated, data-driven decisions.

While IT-focused jobs have been all the rage over the last two decades the rate of growth in the sector has been projected to be about 13% by the Bureau of Labor Statistics. It is still higher than the average rate of growth for all other sectors. However, data science has seen an explosive growth of over 650% since 2012 based on an analysis done on LinkedIn. The role of a Data Scientist has projected forward to one of the most in-demand jobs and ranks second to machine learning engineer- which is a job that is adjacent to a data scientist.

In the upcoming time, Data Scientists will have the ability to take on areas that are business-critical as well as several complex challenges. This will facilitate the businesses to make exponential leaps in the future. Companies in the present are facing a huge shortage of data scientists. However, this is set to change in the future.

The post FUTURE PROSPECTS OF DATA SCIENCE WITH GROWING TECHNOLOGIES appeared first on Artificial Intelligence.

At the time of Artificial Intelligence taking over the world rapidly, does engineering have any future?

Back then engineering was all about blueprints, sketches, and physical models. But today it is intensively about software tools and computer designs. The demand for artificial intelligence and digital technology has been gaining momentum. Advancements in the AI sector are transforming smart systems and supervised machine learning to a great extent.

Artificial intelligence systems will ease the laborious tasks that engineers do such as finding relevant content, fixing errors, and determining solutions. Smart systems can help finish their job quickly. AI and digital tech can also assist the system engineer in creating sophisticated designs along with incorporating sensor-based design procedures and delivering the designs to intelligent manufacturing facilities.

But AI may not approach the project the way a human designer would. Sometimes this can go off the grid too. Because today’s machines are usually made of expert systems that have software-enabled decision-making. Since the engine of this software is based on if-then rules, it is acquired through experience.

When new knowledge is registered in the library, the software uses if-then rules to expand new facts or ifs and comes out suggesting various solutions or then what occurs.

This process serves as the basis of AI and machine learning. As people are connected with the help of the internet, this opens the doors for smart machines to bring in new services and opportunities.

The largest fraction of smart systems is currently made of expert systems and this will be soon taken over by autonomous robots through technology transformation by 2024. There are critics as well as supporters for this trend. Since the number of robotic appliances continues to increase automatically, the cost of the sensors will decrease this becomes the simple reason for artificial intelligence robots to multiply within no time.

The robotic sensors market is estimated to undergo a CAGR of nearly 8% over the forecast period of (2021-2026). Currently, most of the industries like automotive, transportation, industrial manufacturing, logistics, and defense have started to adopt autonomous robotics and digital technology as their main mode of the production process.

As a result, due to this rapid growth of smart technologies with its roots, interconnected artificial intelligence can lead to uncertainty. Though computers can take intelligently-based action, they are not capable of replicating the cognition processes of the human brain. Algorithms of artificial intelligence tech can only deal with known data and cannot predict and formulate rational decisions during uncertain situations.

New technologies using the most advanced AI and machine learning have been coming up due to their widespread connectivity and inexpensive sensors. These technologies are primitive and still are not capable of mimicking the human brain.

It becomes clear that AI algorithms relate facts to solutions that are dependent on experiential learning without any acknowledgment of physics. AI has evolved from scientific advancement to an engineering tool. Latest innovations in digital tech require engineers from various domains to learn and integrate AI tools into their engineering designs.

Many open-source tools such as Microsoft’s DMLT, Google’s TensorFlow, and Amazon’s DSSTNE possess software libraries that empower machine learning. The DeepVariant open AI software of Google can depict a person’s genome from sequencing data more accurately than other methods are helping engineers to seek help from.

Personal productivity assistants like Amazon’s Alexa, Apple’s Siri, and Windows Cortana use natural language processing to make decisions. IBM Watson has been trained by Oncologists to help them treat and diagnose lung cancer. Tesla is getting closer to self-driving autonomous vehicles. Zebra Medical Systems, an Israeli company is developing tools for radiology having greater than human accuracy. All this is possible with the help of different types of engineers who are responsible for training smart systems.

At this point in time, the role of a human engineer may be that of a director shortly rather than producing and manufacturing the products. Though humans may not be executing the task, they are definitely the ones who are choosing the direction the machine should work. Once the machine knows how to design things, the system of engineering will change but engineers will still be highly skilled and relevant.

The uncertain future of technologies demands resilient and versatile engineers who can design robust technologies using artificial intelligence with different skill sets, including teaching AI systems how to innovate and become part of future human-AI organizations.

The post ARTIFICIAL INTELLIGENCE AND THE FUTURE OF ENGINEERING appeared first on Artificial Intelligence.

Here are some prominent predictions regarding the impact of AI on the pharmaceutical industry.

What makes it impossible to imagine the future of the pharmaceutical industry without discussing Artificial Intelligence? What does AI have in store for the industry in the coming years?   

The future of pharmaceuticals is clearly AI-enabled. While the extent of its potential is still being figured out, it is certain that the industry is in for AI-assisted revolutions; experts anticipate a significant impact on drug discovery, research, and development.  

Keep reading to find out how AI will shape the future of pharmacy.  

With the advent of AI, there has been an obvious shift in the paradigm of innovations in the pharmaceutical industry. Over the past five years, the industry has been enjoying the fruits of the technology to discover new drugs, tackle or prevent illnesses, process medical data, and more.  

From preventing epidemics to discovering vaccines, the industry has numerous responsibilities on its shoulders that call for high-quality solutions. But thanks to AI, most of the tasks that traditionally relied on the capacity of human intelligence have now been automated and fast-tracked. Artificial Intelligence is currently key to improving efficiency and expediting the production of drugs. In the future, AI is expected to produce computational workflows that can increase the quality of solutions by several folds.  

Here are some prominent predictions regarding the impact of AI on the pharmaceutical industry: 

AI will be used for more technical advancement 

Pharmaceutical professionals have already recognized the potential of AI to revolutionize the field. AI and Machine Learning are at the forefront of leading the digital future of the industry. Premier pharmaceutical companies have already collaborated with AI vendors; the technology will soon be leveraged more for manufacturing, discovery, and most importantly, research.  

AI will be used for the management of chronic diseases  

Global drug development and pharmaceutical companies are expected to invest more in AI to facilitate the research and discovery of chronic diseases and terminal illnesses. In the US, chronic diseases constitute one of the major reasons for death. It is safe to say that AI will be used to improve the management of chronic diseases, bring down operational costs, and certainly, boost the patient’s health. Some of the chronic diseases that will receive attention in the coming years include cancer, chronic kidney disease, diabetes, and idiopathic pulmonary fibrosis. 

More healthcare companies will invest in AI

Studies show that almost 62 percent of healthcare companies are already planning on investing in AI. By 2025, around 50 percent of healthcare companies are expected to fully utilize the benefits of AI. AI will also be used to boost the success rates of newly discovered drugs. According to a study conducted by MIT, only 13.8 percent of drugs pass the trials. AI can help increase the success rate of drugs while bringing down the cost of operations.

AI will be used to refine the candidate selection process 

AI will redefine the future of pharmaceuticals by fine-tuning the process of candidate selection in clinical trials. It will ensure that the most suitable candidates are chosen for trials by performing a detailed analysis of data and filtering the candidate pool. Factors that impact the accuracy of results can also be filtered to lower errors.  

AI will enhance the process of medical data extraction 

AI and Machine Learning tools are expected to be a lot more accessible in the future than it is now. This will help organizations to screen and diagnose patients with greater accuracy. AI will also allow experts to extract more meaningful information from existing medical data.  

Though AI and ML techniques can be potentially generalized across fields, proper implementation in the pharmaceutical industry requires expertise in respective scientific domains. This in turn increases the demand for professionals with advanced degrees and exposure.  

The post Artificial Intelligence And Pharmacy: The Future appeared first on Artificial Intelligence.

The journey of big data in India has been long, but its influence is increasing rapidly.

Big data plays a principal role in understanding customer needs and drawing market patterns. Every large and small-scale organization needs data analytics to gain a competitive edge in the market and make informed decisions to reduce or eliminate unnecessary risks.

India is one of the fastest-growing countries adopting artificial intelligence, big data analytics, and IoT. According to reports, the Indian analytics industry is estimated to reach approximately US$16 billion by 2025. A recent study reveals that between the years 2021 to 2026, the industry will grow at a CAGR of 35.1%.

• HIRING ALERT! TOP BIG DATA JOB OPENINGS TO APPLY THIS WEEKEND
• NOW HIRING! TOP BIG DATA JOB OPENINGS IN RENOWNED GLOBAL ORGANIZATIONS
• EVERYTHING YOU NEED TO KNOW ABOUT DATA SCIENCE, BIG DATA AND DATA ANALYTICS

Big Data’s Influence in the following Industries:

Healthcare

Big data has proved to be one of the grandest blessings for the Indian healthcare industry. Several hospitals, pharmaceutical companies, R&D centers are utilizing the benefits of big data technologies and predictive analytics to facilitate the best treatments for the patients.

R&D centers and pharma companies are using data analytics for drug development. Using predictive models and statistical tools for drug discovery has reduced the costs for running simulations and trials. Predictive analytics and AI applications can diagnose the future risks a patient might be subjected to and help medical practitioners to take precautions beforehand.

Data analytics has also helped healthcare specialists to create cancer treatments. Predictive analytics is implemented to detect the success rates of different treatments and develop personalized treatment plans to best suit the patients’ needs.

Education

Implementing big data in the education sector has enabled researchers and educators to understand the needs of the students. Educational institutions are leveraging big data to analyze students’ performances based on the different assignments and tasks allotted to them. Monitoring the actions of the students, like the duration each candidate takes to answer a question, the reason behind their ability and inability to answer certain questions, the sources used for examination preparations, and other queries are easily obtained through data analytics. Colleges and universities are now using analytics to develop customized programs to ensure that students can follow the classes and work at their own pace.

Finance

The fintech industry in India is growing rapidly. Indian fintech companies rely heavily on big data analytics to make crucial business decisions and gain a competitive edge in the finance market. The finance and banking industry is aware of the importance of big data. It has helped finance companies reach remote markets in the country and facilitate banking and financial services.

The Future of Big Data in India

The Indian industrial ecosystem is changing. Big data is opening unprecedented opportunities that were unimaginable even a few years ago. The demand for data analytics in India is on the rise. It has led to an increase in the demand for data scientists in the country. Candidates opting for a career in data science should know about big data technologies and tools like Hadoop, Hive, Spark Streaming, and others. Several industries in India, like e-commerce, manufacturing, and retail, have taken up big data to ensure customer satisfaction and business growth. The future of big data in India is bright.

The post THE JOURNEY OF BIG DATA IN INDIA AND ITS FUTURE AHEAD appeared first on Artificial Intelligence.

Digital transformation has aided businesses in embracing change and remaining competitive in an increasingly digital world.

Data is the sole driver of today’s world. It has become the most valuable asset, and without it, one cannot hope to prosper in the present congested market. Companies must efficiently use their data because it can be a differentiating factor for corporate growth. This necessitates the effective integration of data analytics and Artificial Intelligence (AI) into business processes.

Integrating big data analytics allows for faster, more informed decision-making and access to real-time data. It has the ability to be a game-changer in businesses’ efforts to transform digitally. Numerous data analytics services companies are extended their services and helping enterprises in creating new business models and achieving great heights. 

Implementing big data analytics and AI into existing business models can yield real results, allowing firms to undertake a successful digital transformation. The transformation process aims to use digital technology to build or modify consumer experiences, culture, and business processes, to suit changing customer wants and market conditions.

This is where AI comes into play. It has the potential to help businesses create a digital product development life cycle, become more imaginative, agile, and adaptable than ever before.

How Big Data Aids Digital Transformation?

Digital transformation has aided businesses in embracing change and remaining competitive in an increasingly digital world. The usefulness of big data in digital transformation is determined by an organization’s ability to combine the two to enable business process digitization and automation. This digitalization and automation leads to digital transformation in product development, more efficiency, new business models, and innovation.

Businesses can also use big data analytics to get precise information about an individual or various groups of clients. This can include information such as which websites do they spend time on, what they purchase, purchasing frequency, etc. Businesses can use this data to make improvements to meet their customers’ future wants while also creating goals on how to achieve those demands. As a result, to complete their digital transformation, organisations must use big data and data analytics.

Artificial Intelligence and Digital Transformation: What’s the Connection?

When Artificial Intelligence became a part of many organisations’ business goals, the digital transformation went another step forward with the ability to integrate different systems and automate various daily tasks. These technologies are crucial in the digital transformation because they allow your company to make better use of the data it collects in a variety of ways.

As well as resulting in faster and more efficient operations and, as a result, increased output. AI enables us to make use of all of this data to propel the organization forward, whether by improving current products and services or exploring new inventive initiatives.

How Big Data Analytics and AI can help you create value through digitization? 

Emerging technologies, notably AI, are slowly infiltrating the everyday lives of businesses all around the world, much like the internet revolutionised the business world in the past.

Data analytics services for small business and medium businesses are also offered by numerous companies. Having said this, the question is no more when will all small and medium-sized businesses be digitally transformed, but which technologies will be important and prioritised. AI and big data may be employed in a variety of industries by businesses.

Digital transformation must be used to add value to a product, with artificial intelligence detecting information to assist the experts involved in various stages of the digital product development process, such as design, execution, and delivery.

With everything digitalized, you may start looking at the phases of the project that need to be tweaked; for example, AI in the design phase increases research, development, and forecasts the future steps.

In the execution phase, continuous maintenance is an important stage, and this is where AI and Machine Learning can assist. These are real-time replies to the research. When it comes to delivering a product, it’s all about the customer experience. Businesses can leverage data analytics services and solutions to ensure they are on the right track.

Much has been stated about how AI and Big Data Analytics can enable businesses to stay ahead of the curve in their digital transformation, giving them a competitive advantage over their competitors. Let’s take a look at why organisations need to be digital in product developmentand why should they use big data analytics and artificial intelligence in their digital transformation efforts:

• Using Data Analytics Services, businesses can readily measure and model risks using big data tools.
• By automating and optimising common procedures and procedures, businesses can save time and money.
• Many big data analysis technologies enable businesses to have a deeper understanding of their customers and actively develop loyal customers rather than simply responding to their needs and inquiries.
• Create quicker business decisions based on cognitive technology outputs.
• Businesses can learn about customer-related trends and patterns and focus on client retention by utilising big data analytics.

In a nutshell, big data analytics and AI may be used in a variety of ways to assist businesses in achieving successful digital transformation. Companies must keep in mind, however, that digital business transformation is a continuous process, and they must continue to come up with new strategies and leverage big data analytics services for enhancing business intelligence and keep up with the changing environment even after they have achieved success.

The post BIG DATA ANALYTICS AND AI WILL DRIVE THE FUTURE OF DIGITAL TRANSFORMATION appeared first on Artificial Intelligence.

UNIVERSITY PARK, Pa. — In smart cities of the future, sensors distributed throughout buildings and bridges could monitor infrastructure health. Cloud-based computing could decrease traffic with real-time analysis available to commuters. Windows could tint themselves darker on sunny days or lighten to brighten a room on cloudy ones. 

None of these innovations, however, can materialize without managing the enormous amounts of data generated by robust sensing networks, according to an interdisciplinary team of Penn State researchers. They published a perspective article on March 19 in Science, highlighting smart materials that can sense environmental changes and respond accordingly — without externally transferring data — as one avenue to avoid this data overload.

Science also released a podcast featuring the work on March 18, outlining the benefits of implementing smart materials in tomorrow’s cities, ranging from self-healing concrete structures to building materials that can solve complex equations. 

“Since this problem sits at the intersection of materials science, structural health monitoring and computation, collaboration was important from the get-go,” said Rebecca Napolitano, assistant professor of architectural engineering, who co-wrote the article. 

Napolitano collaborated with Wesley Reinhart, assistant professor of materials science and engineering in the College of Earth and Mineral Sciences, and Juan Pablo Gevaudan, affiliate professor of architectural engineering and Marie Sklodowska-Curie Research Fellow at the University of Leeds. 

Napolitano’s interest in smart cities began during her graduate studies, when she wondered how historic buildings would be accommodated in cities of the future. Her current research, focused on supporting merging infrastructures via computational methods, led her to collaborate with Gevaudan. He investigates how modern concrete materials degrade to better engineer their response and ability to adapt to certain conditions in new and existing buildings. The two researchers lead the adaptive architecture research area in the Convergence Center for Living Multifunctional Material Systems at Penn State.

Reinhart and Napolitano previously collaborated to investigate building damage at the micro- and macroscales through computation. Reinhart works on metamaterials, which are materials engineered to gain unique properties from their structure, and their potential applications in infrastructure. For example, such a metamaterial could sense the path of the sun and perform a calculation to adjust a solar panel accordingly and optimize the energy stored. 

The researchers plan to continue exploring avenues for developing these materials and robust computational methods to optimize them. With further research, according to the researchers, the implementation of smart materials could increase the lifetime of buildings and civil structures, reduce energy consumption and reduce waste from production and use of electronic sensors — all on a citywide scale.

The post Smart materials could pose solution for big-data bottleneck in future cities appeared first on Artificial Intelligence.

Quantum AI and quantum computing are transformational technologies enabling a revolutionary future.

Quantum AI refers to the use of quantum computing for the computation of machine learning algorithms. With the computational advantages of quantum computing, quantum AI can now achieve results that were not possible with classical computers.

Alan Turing published a paper on Computing Machinery and Intelligence in 1950, and since then computers have come a long way. In the current modern age, computer limitations are gradually fading away, and machine learning has the ability to learn from its experiences. Traditionally, this type of intelligence was only achievable by using multiple computers and complicated machine learning algorithms. However, Nature Nanotechnology journal had a paper published recently where scientists proposed a new method – designing a computer with embedded intelligence and using the atom’s quantum spins to revolutionize computing as we know.

Next-Gen Computing

To understand this concept, let cover the basics of neuromorphic computing. In layman’s language, neuromorphic computing attempts to imitate the way a human brain works. From a technical perspective, neuromorphic computing is concerned with computer engineering where the elements of a computer, both hardware, and software, are wired according to the human nervous system and cerebral system.

Engineers study several disciplines like computer science, biology, mathematics, electronic engineering, and physics to create accurate neural structures. Neuromorphic computing aims to create devices that can learn, retain information, and make logical deductions the way a human brain does, a cognition machine. Alongside, it also attempts to prove how the human brain works by scavenging new information.

As a step forward in artificial intelligence technology, neuromorphic computing allows robots embedded with small computing hardware to make their own decisions in the future.

The Quantum Brain

The Quantum brain is a prime example of neuromorphic computing, the future of computing. Our human brains use signals sent by our neurons to make all kinds of computations. Similarly, the quantum brain uses cobalt atoms on a superconducting black phosphorus surface to imitate the process of human brain signals.

Cobalt atoms have quantum properties like unique spin states which carry information to stimulate ‘neuron firing’ with applied voltages. This helped the atoms to achieve a self-adaptive behavior based on the external stimuli.

Can AI Work With A Quantum Brain?

Artificial intelligence is an evolving technology, but it still has not overcome technological limitations. But with quantum computing, obstacles to achieving artificial general intelligence, AGI, can be discarded. Quantum computing can rapidly train machine learning models to generate optimized algorithms. Quantum computing can power an optimized and steady AI to complete analysis in a short time, as opposed to years of work that would delay any and all technological advancements.

According to researchers, a realistic aim for quantum AI is to replace traditional algorithms with quantum algorithms. These quantum algorithms can have several use cases to further advancements.

• Developing quantum algorithms for traditional learning models can provide possible boosts to the deep learning training process. Quantum computing can help machine learning by presenting the optimal solution set of the weights of artificial neural networks, quickly.

• When traditional decision-making problems are formulated with decision trees, the next course of action to reach the solution sets is by creating branches for a particular point. However, this method becomes complicated when the problem is too complex. Quantum algorithms can solve the problem faster.

Can neuroscience-inspired quantum computing and AI mesh? Yes, says several similarities between the brain and machine learning techniques like deep learning. Is that future near? Yes and no. Right now, the quantum AI industry needs to work to eliminate immaturities in the technology and achieve crucial milestones such as less error-prone and more powerful computing, developing the right AI applications where quantum computing can outperform traditional computing, and creating a widely adopted open-source modeling and training frameworks. These milestones will push quantum AI towards future developments.

The post QUANTUM AI & QUANTUM BRAIN: THE IMITATION GAME OF THE FUTURE appeared first on Artificial Intelligence.