Sharing patient data by applying machine learning in hospitals

Federated learning is a new and promising approach to make the adoption of machine learning and patient data sharing possible in the medical field.

Despite the widely popular application of AI and Machine Learning across industries, the healthcare sector always hesitates to embrace the technology of the future due to privacy problems. The concern for patient confidentiality has always been the dilemma impeding the adoption of machine learning or the potentials and the power to transform the healthcare landscape.

Sharing patient data by applying machine learning (Photo: HealthCatalyst)

Federated learning comes as the solutions for the dilemma as the technology generates almost identical results as the method that is not protected does. Federated learning was first used by Google to train an algorithm through several decentralized computers containing local data samples, without sharing them. And the method shows great potentials in brain imaging,  analyzing magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.

For instance, doctors in different parts of the world can enter their patient scan data, training on a shared model, then the new model will be transferred to a centralized one. By doing that, the model has gained the knowledge front he hospital and generate useful clinical data.  "Traditionally, machine learning has used data from a single institution, and then it became apparent that those models do not perform or generalize well on data from other institutions.", said Spyridon Bakas.

In regard to tumor boundaries, Bakas the opinion not only varies from person to person but also differs from one day to another of the same doctor. "Artificial Intelligence allows a physician to have more precise information about where a tumor ends, which directly affects a patient's treatment and prognosis.". He said.

In the research to study the effectiveness of the new technology, the outcomes that have been produced by federated learning are little to no significant differences compared to the other methods. The technology opens a new promising field that needs more study, expanding the application of the use cases and industries other than healthcare.

Source: Science Daily.

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AI to enhance customer experience (Part 2)

Artificial intelligence is expected to transform the industry landscapes ranging from retail, healthcare to manufacturing. 

With AI, businesses from a number of sectors are optimizing consumer service by speeding the processing of activities, gaining more insight into customer attitudes and expectations, and offering innovative applications to improve customer experience.

Making online car purchasing easier

For the automotive industry, the in-person customer experience is key in convincing a potential lead to come to a buying decision. Given the current pandemic situation where people are trying their best to avoid human-to-human interaction, technologies preventing the risk of contraction thrive across all sectors including. And AI strategy plays an important role in transforming the marketing digital channel of car dealers to attract and approaching customers.

Many customers who do not own a vehicle are now considering owning one because of limited or abandoned use of public transport, and are looking to perform purchases online and reasonably quickly. The application of AI tools including an online chatbot that invites shoppers addresses general questions and gathers lead information on behalf of car dealers to direct customers to relevant information based on their interests and needs. The algorithm is being constantly changed and evolved to help clients find the right choices.

Making online car purchasing easier (Photo: Autocar)

Moving data entry to bots to add efficiency

As part of its customer engagement policy, healthcare providers have used automation to simplify the delivery of acute and outpatient registrations and therapeutic forms. 

Robotic process automation (RPA) is being adopted to deliver customized texts to patients with the clinical forms required for various types of visits. “We are reducing the time needed in clinic ahead of the visit, which provides tremendous patient satisfaction. Our patients like completing this work when it’s convenient for them.”, Smith said. In another word, the healthcare industry is moving the role of data entry from conventional caregivers to bots, resulting in substantial efficiencies and savings.

Source: CIO ASEAN

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Artificial Intelligence Translates Thoughts Into Text Using Brain Implant

 Researchers at the leading university had developed the AI to decipher up to 250 words in real-time from a set of between 30 and 50 sentences.

Mind-reading AI can turn the neural activity into sentences with a 97 percent accuracy rate. Scientists have developed an artificial intelligence system that can translate a person’s thoughts into text by analyzing their brain activity.  The algorithm was trained using the neural signals of four women with electrodes implanted in their brains, which were already in place to monitor epileptic seizures.

The volunteers repeatedly read sentences aloud while the researchers fed the brain data to the AI to unpick patterns that could be associated with individual words. The average word error rate across a repeated set was as low as 3 percent.

“A decade after the speech was first decoded from human brain signals, accuracy and speed remain far below that of natural speech,” states a paper detailing the research, published this week in the journal Nature Neuroscience.

“Taking a cue from recent advances in machine translation, we trained a recurrent neural network to encode each sentence-length sequence of neural activity into an abstract representation, and then to decode this representation, word by word, into an English sentence.”

AI now can translate thoughts into text using a brain implant. (Photo: Newscientist.com)

The average active vocabulary of an English speaker is estimated to be around 20,000 words, meaning the system is a long way off being able to understand regular speech. Researchers are unsure about how well it will scale up, as the decoder relies on learning the structure of a sentence and using it to improve its predictions. This means that each new word increases the number of possible sentences, therefore reducing the overall accuracy.

“Although we should like the decoder to learn and to exploit the regularities of the language, it remains to show how much data would be required to expand from our tiny languages to a more general form of English,” the paper states. One possibility could be to combine it with other brain-computer interface technologies that use different types of implants and algorithms.

Last year, a report by the Royal Society claimed that neural interfaces linking human brains to computers will enable mind reading between people. The report cited technologies currently being developed by Elon Musk’s Neuralink startup and Facebook, who describe cyborg telepathy as “the next great wave in human-oriented computing”.

The Royal Society estimated that such interfaces will be an “established option” for treating diseases like Alzheimer’s within two decades.

“People could become telepathic to some degree, able to converse not only without speaking but without words,” the report stated while expanding on more futuristic applications like being able to virtually taste and smell without physically experiencing the sensation.

Source: Independent UK

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Software prevents accidents from autonomous vehicles (Part 2)

Before participating in road traffic, autonomous vehicles must demonstrate that they do not harm the others on the road. New software prevents accidents by predicting different variants of a traffic situation every millisecond. The other methodologies are shown in part 2 as below.

Using software to prevent accidents from autonomous vehicles (Photo: Emerj)

Streamlined models for swift calculations

This kind of detailed traffic situation forecasting was previously considered too time-consuming and thus impractical. But now, the Munich research team has shown not only the theoretical viability of real-time data analysis with simultaneous simulation of future traffic events: They have also demonstrated that it delivers reliable results.

The quick calculations are made possible by simplified dynamic models. So-called reachability analysis is used to calculate potential future positions a car or a pedestrian might assume. When all characteristics of the road users are taken into account, the calculations become prohibitively time-consuming. That is why Althoff and his team work with simplified models. These are superior to the real ones in terms of their range of motion -- yet, mathematically easier to handle. This enhanced freedom of movement allows the models to depict a larger number of possible positions but includes the subset of positions expected for actual road users.

Real traffic data for a virtual test environment

For their evaluation, the computer scientists created a virtual model based on real data they had collected during test drives with an autonomous vehicle in Munich. This allowed them to craft a test environment that closely reflects everyday traffic scenarios. "Using the simulations, we were able to establish that the safety module does not lead to any loss of performance in terms of driving behavior, the predictive calculations are correct, accidents are prevented, and in emergency situations the vehicle is demonstrably brought to a safe stop," Althoff sums up.

The computer scientist emphasizes that the new security software could simplify the development of autonomous vehicles because it can be combined with all standard motion control programs.

Source: Science Daily.

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AI to enhance customer experience (Part 1)

 AI is expected to help improve digital customer experience during the pandemic.

Virtual waiting rooms and texting chatbots

In virtual waiting rooms, Mobile chatbots connect with patients in a manner that help them with digitized forms, remote control capability for both telehealth and in-person visits. The initiative is part of the intention to provide treatment to patients in response to the pandemic. Healthcare companies need to rapidly evolve and make sure that all patients can see their physicians through secure, private and accessible networks, said Jeff Johnson, Vice President of Digital Business at Banner Health.

The mobile chatbot assistant has replaced the traditional pre-visit process of staying in waiting rooms, filling out forms, etc. Chatbots can now interact with patients via their mobile devices and desktop monitor to substitute tasks that were previously done by humans. Digital triaging is another application of AI in healthcare. While people engage with the symptom checker, AI will present them with the potential causes of the symptoms and how they can better be treated, whether it is at one of the hospital emergency locations, a primary care practitioner in their medical network, emergency departments, or by self-management.

Chatbots are being applied in hospitals to aid with the overload situation (Photo: Take)

Analyzing online actions to enhance service

The digital transformation has pushed businesses to transform their customer loyalty through e-commerce strategy. Retailers have begun using AI-based tools to understand their customers through online behaviors. But it was the pandemic that drives the customers to online shopping, urging vendors to try even harder to examine how their customers interact with the website.

The adoption of AI technologies has been integrated into every step of the customer journey, including all the details, actions on the user interface. 

Though the idea seems promising in the retail industry, it is often perceived doubtful by vendors since there is little real-life application of these technologies, especially AI and deep learning that bring measurable outcomes. However, more and more retailers are now willing to take risks with the hope of transforming their business or, better yet, reverse the impacts of the pandemic on their operation in general.  

Source: CIO ASEAN

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Technologies to transform business operations during the pandemic (Part 2)

Besides 5G, IoT and automation, key technologies such as AI and data alternation will play a crucial role in business operations.

Within a short period, companies have embraced remote working for their workers, pushing organizational improvements that would have taken years to prepare and carry out. This changed the attention of the company to the importance of adopting technologies in its operations across industries.

The growth of the open data movement and the realization that public sharing of data provides social and economic benefits might contribute to the opening up of many of these knowledge sources for corporations to innovate. Data exchanges like Terbine and Dawex are building the infrastructure required to inject liquidity into this evolving market.

key technologies such as AI and data alternation will play a crucial role in business operations (Photo: Neuromation)

AI and alternative data

Small enterprises have been lagging behind in harnessing the economic advantages of AI due to the shortage of sufficient data inputs for their algorithms. Only big companies were able to leverage these technologies since they had internal databases resulting from their activities or could afford to purchase data as needed. However, data ownership is not necessarily meant to create data-driven goods and services, as some creative uses of data indicate.

A common approach to the use of AI is data synthesis when developers search for rich datasets to train their programs. Continental, a car component supplier, is estimated to create an AI-based program that can produce 5,000 miles of vehicle test data per hour. Since collecting data with an actual vehicle would have taken as long as 20 days, Intel Labs and Darmstadt University in Germany have utilized the video game Grand Theft Auto to do such thing, helping help train autonomous vehicles.

To evaluate the effects of COVID-19 on the economy, specifically decide which stocks to purchase or sell, many major banks have been trying new approaches regarding data sources. For example, it is better to buy and leverage travel websites, insurance companies and job sites than those from the monthly government statistics and companies’ financial reports. 

In response to challenges posed by the current lockdowns, companies are realizing the vital role of evaluating and predicting the future of the economy post COVID. Enterprises now are putting more time and effort into utilizing and assessing the data to make better predictions as businesses will not be back to the normal before the pandemic. 

Source: CIO ASEAN

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Technologies to transform business operations during the pandemic (Part 1)

The pandemic is pushing organizations to drive innovations in their existing operations in response to the impact of COVID-19.

Drive to automation

The adoption of robots in factories and warehouse has long been a norm in the industry, only now do the organizations realize the crucial role of automation in transforming their operation during COVID-19. However, the need for social distance and safe workspaces has contributed to a major rise in the market for robotic systems and helpers. Firms like Brain Corp, Xenex and Simbe Robotics are hiring extra personnel to create and manage robotic stores and hospitals that now need to sanitize buildings and supermarket shelves.

As these systems help keep employees distant from each other, they also produce data that is used to streamline workflows and business planning. For instants, Simbe's Tally robots gather real-time data on where items are stored on shelves, helping consumers find what they're searching for less amount of time. Not only do these robotic solutions help boost customer experience, but they also keep on collecting new data, minimize operational costs.

Technologies to transform business operations (Photo: Raconteur)

5G and the IoT

The emerging preference towards remote work is expected to accelerate the introduction of 5G networks and facilities as soon as immediate restrictions on travel are removed. The importance of high speed and low latency connectivity has been evident to home staff who are dealing with their difficulties in video conferencing and remote communication. 2.6 billion 5G subscriptions by 2025 and up from 13 million at the end of 2019 are expected, according to Ericsson.  This will encourage businesses to deliver more immersive consumer service across VR and AR in terms of creativity and product growth. Across B2B and B2C verticals, the data created by these value-added technologies can expand consumer interactions as user desires and actions are recorded in real-time and returned to optimized offerings.

IoT initiatives such as remote data collection on environmental conditions in buildings and communities are becoming increasingly crucial as policymakers and decision-makers pursue information on the activity and actions of people under lockdown.

Source: CIO ASEAN

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Review cybersecurity threats and solutions in 2020 (Part 2)

The past decade has seen the proliferation of technology providing many advantages, but also with distinct disadvantages. This part will mention some of the solutions helping your organization defense before cyberattacks.

As threats change over time, security solutions change too. These tools enable security teams to adapt to new attack methods and tools and ideally enable organizations to identify vulnerabilities before attackers can exploit them.

Endpoint Management

As an organization’s systems grow, the expansion of endpoints often occurs in the following categories: cloud solutions, bring your own device (BYOD) policies, Internet of things (IoT) devices. These things mentioned above presents a possibly vulnerable endpoint.

XDR is a trending solution that can help with endpoint management. It provides proactive protection for threat detection and response and offers visibility across networks, endpoints, and clouds. This will increase the productivity of security teams and track threats throughout the organization.

The Growth of Alternative Authentication

Privacy and password strength is a security concern that has persisted since the beginning of the Internet. However, another authentication method is beginning to replace passwords —  alternative or passwordless authentication. 

Alternative authentication solutions, such as hardware tokens, knowledge-based authentication, biometric authentication, and unique password generators can offer a more secure option. It is predicted that by 2022 90% of midsize businesses will adopt passwordless authentication for more than 50% of their use cases.

Companies need to begin investing more to improve in-house cybersecurity expertise. (Photo: 10BestOnline)

Cyber Insurance

The total losses associated with cyberattacks and extortion were estimated at around $11 million in 2019. Due to this, it’s not surprising that organizations are looking for damage reduction measures and buying insurance. 

Despite the need for coverage, insurance providers have struggled to adapt to the changing cyber risk landscape. This includes the increasingly important role that IoT is playing and the emergence of more connected devices. This lag creates a lot of opportunity for new insurance models and coverage. 

Cloud-Based Security Expansion

As the world moves a large amount of technology infrastructure to the cloud, cloud-based security platforms and services continue to grow. Cloud-based security can offer several advantages over traditional approaches, including economies of scale, reduced costs, better protection performance, increased threat intelligence, faster compliance with industry, and government standards.

Advances in Encryption

As cyberattacks have become more sophisticated, numerous advances in data encryption have followed suit, however, a particular encryption strategy is no longer effective. To address this security gap, many advancements in encryption are emerging. These include distributed ledgers, zero-knowledge technologies, ring signing, and privacy technologies. When used in combination, these technologies can provide complete or partial data anonymity and identity and data verification can be automated.

In 2020, cybersecurity trends are turning into a necessity for business continuity, as organizations face attacks from a staggering number of directions.

Source: Dzone

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3 types of AI: a guide to narrow, general, and super artificial intelligence (part 2)

Part 2 of this article will cover the other two types of Artificial Intelligence - general AI and artificial superintelligence.

AI grows to be so close to human thoughts and perceptions. (Photo: Apro Software)

Artificial General Intelligence (AGI) / Strong AI / Deep AI

The idea of Artificial general intelligence (AGI), also known as strong AI or deep AI, is the system with general intelligence that simulates human intellect and actions. Under particular situations, AGI can think, comprehend and behave in a manner that is indistinguishable from that of a human being.

Finding a way to make machines conscious and programming a full set of cognitive abilities are the main barriers preventing scientists from achieving Deep AI. Apart from enhancing performance on singular tasks, machines will have to push experiential learning to the next level to apply the knowledge to a broader variety of challenges. Though knowing Deep AI follows the theory of mind AI framework training machines to understand humans, the lack of knowledge on the functionality of the human brain has refrained researchers from doing so. 

It is more likely to see an improvement in the ability to learn of a machine than achieve a complete Artificial General Intelligence (AGI). For example, the effort to achieve AGI of one of the fastest supercomputers, Fujitsu-built K, has failed to yield any significant results given that it took 40 minutes to replicate a single second of neural activity.

Artificial Superintelligence (ASI)

Artificial Super-Intelligence (ASI) is a conceptual AI that not only mimics or recognizes human intellect and behavior but also becomes self-conscious and transcends human intelligence and capacity. 

In ASI, artificial intelligence grows to be so close to human thoughts and perceptions that it not only recognizes them but evokes feelings, wishes, values and desires of its own. besides the ability to emulate everything human beings can do including math, science, sports, art, medicine, hobbies, emotional relationships, ASI will have a better memory and quicker capacity to interpret and evaluate data and stimuli. 

Source: Codebots

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3 types of AI: a guide to narrow, general, and super artificial intelligence (part 1)

Part 1 of this article will go through a brief overview of Artificial Intelligence and the first type of AI.

Narrow or weak AI, general or strong AI and artificial superintelligence are the three types of artificial intelligence (AI).

Right now, we have only achieved narrow AI. As machine learning capabilities continue to develop, and scientists are getting closer to achieve general AI in the near future.

3 types of AI: a guide to narrow, general, and super artificial intelligence (Photo: accilium)

Artificial Narrow Intelligence (ANI) / Weak AI / Narrow AI

The only successful form of AI that has been implemented today is Artificial narrow intelligence (ANI), also known as weak AI or narrow AI. Though Narrow AI is excellent at completing specific tasks such as facial recognition, speech recognition/voice assistants, driving a car, or searching the internet, it is meant to perform singular tasks.

Intelligent as these machines appear, they only simulate simple human behaviors and function under a limited range of constraints. The fact that Narrow AI does not imitate or replicate human intelligence and work on a restricted set of parameters and contexts makes them a weak type of AI. The applications of this type can be seen at the speech and language recognition of the Siri on iPhones,  vision recognition of self-driving cars, and recommendation engines that make suggestions based on purchase history.

In the last decade, Narrow AI has undergone several breakthroughs, propelled by advancements in machine learning and deep learning. AI systems are now used in medicine to diagnose cancer and other diseases with a high degree of precision by emulating human cognition and reasoning.

Natural language processing (NLP) emerges in the Narrow AI's machine intelligence which recognizes speech and text in chatbots. Hence, AI is designed to communicate with humans in a natural, personalized way.

Narrow AI consists of reactive AI which has no memory or data storage to simulate human responses to things and limited memory AI which can use historical data to make decisions with equipped learning capabilities. The vast majority of AI nowadays is limited memory that utilizes deep learning technology to personalize users’ experience.

Source: Codebots

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Software prevents accidents from autonomous vehicles (Part 1)

Before participating in road traffic, autonomous vehicles must demonstrate that they do not harm the others on the road. New software prevents accidents by predicting different variants of a traffic situation every millisecond. One of these methodologies called algorithm is mentioned in part 1. 

A vehicle moves toward a convergence. Another vehicle streams out of the go across the road; however, it isn't yet certain whether it will turn right or left. Simultaneously, a passerby ventures into the path legitimately before the vehicle, and there is a cyclist on the opposite side of the road. Individuals with street traffic experience will when all is said in the done survey, the developments of other traffic members virtually.

"These sorts of circumstances present a gigantic test for self-ruling vehicles constrained by PC programs," clarifies Matthias Althoff, Professor of Cyber-Physical Systems at TUM. "However, independent driving will possibly pick up the acknowledgement of the overall population on the off chance that you can guarantee that the vehicles won't jeopardize other street clients - regardless of how confounding the traffic circumstance."

Using software to prevent accidents from autonomous vehicles (Photo: IIoT World)

Algorithms that peer into the future

A definitive objective when creating programming for self-governing vehicles is to guarantee that they won't cause mishaps. Althoff, who is an individual from the Munich School of Robotics and Machine Intelligence, and his group have now built up a product module that for all time investigates and predicts occasions while driving. Vehicle sensor information is recorded and assessed each millisecond. The product can compute all potential developments for each traffic member - if they hold fast to the street traffic guidelines - permitting the framework to look three to six seconds into what's to come.

In light of these future situations, the framework decides an assortment of development choices for the vehicle. Simultaneously, the program ascertains potential crisis moves in which the vehicle can be moved out of damage's way by quickening or slowing down without imperiling others. The self-ruling vehicle may follow courses that are liberated from predictable impacts and for which a crisis move alternative has been recognized.

Source: Science Daily.

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