Patient hesitancy to seek care during the pandemic created the perfect storm for delayed care. But big data and analytics are driving population health at Southwestern Health Resources to close care gaps.

“There has been a dramatic decrease in willingness of people to seek healthcare, whether it’s for urgent, critical medical needs or routine screening,” Andrew Ziskind, MD, senior executive officer of Southwestern Health Resources told HealthITAnalytics. “Right now, there’s a public health crisis in the short term, but in the long term, there will be tens of thousands of new cancer cases because of a lack of screening.”

Four in ten adults reported avoiding care because of COVID-19, according to recent data from the Centers for Disease Control and Prevention. So closing care gaps required innovative thinking to manage populations. At the center of this strategy is actionable data.

“The first thing we can do for our existing members is to identify who has gaps,” Ziskind explained. “Our data is robust enough that we can see where the targets are geographically, age-wise, and so forth.”

As a clinically integrated network, Southwestern Health Resources has access to claims data and clinical data to inform these decisions.

“Claims data is a lagging indicator,” Ziskind argued. “But clinical data is probably the most important advantage of being a provider-based clinically integrated network. All of our primary care physicians are connected to us through a common electronic medical record.”

This connectedness allows for easy data sharing.

At traditional health systems, patients with diabetes who might have seen an ophthalmologist and closed a care gap during that visit may have forgotten to inform their primary care provider. While the gap in care is technically closed, the provider is unaware.

But an integrated data network eliminates this problem as the primary care providers can have access to all of the patient’s records.

“Documentation around gap closure is often very challenging. The more we can mine the data and identify alternatives, the better,” continued Ziskind. “We’re using the breadth of data that we have access to for identifying where the gaps are. Once we know what they are, we can then use a gap-targeted approach for each specific one.”

Southwestern Health Resources took a multi-pronged approach to targeting patients and closing their gaps in care. The network began by calling each patient with unfilled gaps, but the call center team members saw very low response rates.

“Patients are suspicious about phone calls. They sometimes are confused as to if the call is from the hospital or health system or insurance company,” highlighted Ziskind. “We found that the highest success rate is if the patient is contacted on behalf of their physician.”

Patients then had the option to seek care in person or have a provider come to their home. Patients who opted for in-office visits were given instructions on how to make an appointment and Southwestern Health facilitated services at home if the patient preferred.

“You have to customize at the level of the individual patient,” Ziskind emphasized. “We tried to get rid of any patient burden.”

Ensuring the information was culturally component was also critical. Not only does this include translating information into multiple languages, but it also means delivering messaging in a way that best suits patient need.

“There’s a component through local churches and community access. There’s traditional mail. There’s email. There’s social media,” Ziskind highlighted. “We’re really trying to take a multi-prong approach to enhancing awareness.”

These efforts began with data that allowed Southwestern Health Resources to identify gaps in care and thrived when data on individual patient preference was actionable. Customizing outreach improved gap closure and gave providers actionable information.

As gap closure efforts continue, Ziskind and team plan to focus on clinically relevant patient outcomes, including promoting preventive health screenings.

“More and more we’re trying to move upstream in the disease process. We’re focusing on risking risk as opposed to just the management of patients who have advanced, complex disease,” he concluded. “The earlier we can detect disease, the better the long-term outcome will be for the patient.”

The post How Big Data, Analytics Drives Population Health, Closes Care Gaps appeared first on Artificial Intelligence.

Medical wearables, kitchen appliances and fitness equipment and other connected devices are regularly connecting to corporate networks, prompting technology leaders to warn that significant action should be taken to protect them from being used to hack into businesses.

That’s according to a new survey on practices for securing IoT (the Internet of Things) commissioned by Palo Alto Networks, the global cybersecurity leader. It polled 1,350 IT business decision makers in 14 countries in Asia including India, Europe, the Middle East and North America.

Overwhelmingly, respondents report a rise in the number of IoT devices connecting to their networks over the last year. Among the connected trash cans, light bulbs and hand sanitizers, one red flag emerged: More than half of those who polled said they either need to make a lot of improvements to the way they approach IoT security (43 percent), or that a complete overhaul is needed (36 percent).

In India, businesses surveyed have complete confidence (92%) that they have visibility of all the IoT devices connecting to their organisation’s network. However, two in five (40%) of the largest businesses surveyed (3,000 employees plus) reported that they have not segmented IoT devices onto separate networks — a fundamental practice for building safe smart networks. It is even more worrisome that only 27 percent reported following best practices of using micro-segmentation to contain IoT devices to their own tightly controlled security zones.

“Adoption of digital technologies is being seen on an unprecedented scale and so is the spike in security concerns. We need to bear in mind that cybercriminals are getting smarter, thanks to technological advancements and innovations. They are constantly exploring new avenues of cyberattacks – including through IoT devices – and therefore, more caution needs to be exercised by the organisations as they take a technological leap,” said Anil Bhasin, regional vice president, India & SAARC, Palo Alto Networks. He further added, “As we plan on a wider implementation of the Internet of Things (IoT), there has to be a solid strategy in place to overcome the security challenges that come along.”

“Traditional networks are ill-equipped to handle the surge in adoption of IoT devices,” said Tanner Johnson, senior cybersecurity analyst at Omdia. “Device behavior baselines need to be established to allow for new recommended policies to help stop malicious activity. For instance, it would raise a flag if a connected thermostat started transmitting gigabytes of data to an unfamiliar site.”

Palo Alto Networks released the survey as part of its ongoing effort to shed light on security threats posed by the surge in deployment of devices connected to the internet. Business Insider Intelligence forecasts there will be more than 41 billion IoT devices by 2027, up from 8 billion last year.

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Microsoft Corp. today previewed a new Azure instance for training artificial intelligence models that targets the emerging class of advanced, ultra-large neural networks being pioneered by the likes of OpenAI.

The instance, called the ND A100 v4, is being touted by Microsoft as its most powerful AI-optimized virtual machine to date.

The ND A100 v4 aims to address an important new trend in AI development. Engineers usually develop a separate machine learning model for every use case they seek to automate, but recently, a shift has started toward building one big, multipurpose model and customizing it for multiple use cases. One notable example of such an AI is the OpenAI research group’s GPT-3 model, whose 175 billion learning parameters allow it to perform tasks as varied as searching the web and writing code.

Microsoft is one of OpenAI’s top corporate backers. The company has also adopted the multipurpose AI approach internally, disclosing in the instance announcement today that such large AI models are used to power features across Bing and Outlook.

The ND A100 v4 is aimed at helping other companies train their own supersized neural networks by providing eight of Nvidia Corp.’s latest A100 graphics processing units per instance. Customers can link multiple ND A100 v4 instances together to create an AI training cluster with up to “thousands” of GPUs.

Microsoft didn’t specify exactly how many GPUs are supported. But even at the low end of the possible range, assuming a cluster with a graphics card count in the low four figures, the performance is likely not far behind that of a small supercomputer. Earlier this year, Microsoft built an Azure cluster for OpenAI that qualified as one of the world’s top five supercomputers, and that cluster had 10,000 GPUs.

In the new ND A100 v4 instance, what facilitates the ability to cluster together GPUs is a dedicated 200-gigabit per second InfiniBand network link provisions to each chip. These connections allow the graphics cards to communicate with each across instances. The speed at which GPUs can share data is a big factor in how fast they can process that data, and Microsoft says its the ND A100 v4 VM offers 16 times more GPU-to-GPU bandwidth than any other major public cloud.

The InfiniBand connections are powered by networking gear supplied by Nvidia’s Mellanox unit. To support the eight onboard GPUs, the new instance also packs a central processing unit from Advanced Micro Devices Inc.’s second-generation Epyc series of server processors.

The end result is what the company describes as a big jump in AI training performance. “Most customers will see an immediate boost of 2x to 3x compute performance over the previous generation of systems based on Nvidia V100 GPUs with no engineering work,” Ian Finder, a senior program manager at Azure, wrote in a blog post. He added that some customers may see performance improve by up to 20 times in some cases.

Microsoft’s decision to use Nvidia chips and Mellanox gear to power the instance shows how chipmaker is already reaping dividends from its $6.9 billion acquisition of Mellanox, which closed this year. Microsoft’s own investments in AI and related develop have likewise helped it win customers. Today’s debut of the new AI instance was preceded by the Tuesday announcement that the U.S. Energy Department has partnered with the tech giant to develop AI disaster response tools on Azure.

The post Microsoft targets its fastest Azure AI instance to date at large neural networks appeared first on Artificial Intelligence.

‘Big Data’ has become a staple of scientific endeavour. The phrase crops up repeatedly in common parlance. It has become a buzzword synonymous with scientific effort, experimental complexity and data reliability. This is because the field of ‘Big Data’ offers ways how to deal with large datasets often originating from different experiments.

For example, an experiment to sequence your DNA may generate over 200GB of data. That is the size of two full-length 4K movies!

Big Data increases the robustness of any findings and reduces the odds of a result being only due to chance.

In molecular biology, if a scientific result is supported by various sources of data, such as DNA, protein and other molecular research, then it would be expected to provide a better overall understanding of the natural phenomena under study.

However, to generate and analyse such ‘Big Data’, scientists are required to train in both wet-lab and dry-lab techniques. ‘Wet-lab’ scientists perform research on chemical and biological samples of various origins, including patients. ‘Dry-lab’ scientists mine the large datasets generated from a multitude of experiments computationally. They combine all the elements together to identify trends, create models and provide unique insight that no single source of data can achieve.

This kind of research integration has many applications. Networks can be built to understand the relationships between genes or proteins. Libraries of synthesised compounds can be screened in search of potential drugs. Diagnostic biomarkers may be identified by combining DNA sequencing and protein datasets. On a large scale this is beneficial to patients in the form of earlier diagnosis, personalised treatment and improved outcome.

Modern biomedical research labs rarely conduct purely wet- or dry-research. The scientist of the 21st century needs to be skilled in both types of research. Thus, prospective biomedical students are encouraged to try their hand at both dry- and wet-lab techniques. This enables them to find their place in the research community of the future, doing research that they enjoy.

The University of Malta offers several courses which train students in dry- and wet-lab skills and research.

Jean Paul Ebejer is the coordinator for the Master’s in Bioinformatics programme and performs research in bioinformatics and computer-aided drug design. Byron Baron is involved in the BSc for medical biochemistry and heads research in methylation proteomic analysis on cancer.

Both lecture and research at the Centre for Molecular Medicine and Biobanking of the University of Malta.

Sound bites

• A groundbreaking study is the first to analyse the relationship between group behaviours, group type, group dynamics, and kinship of beluga whales in 10 locations across the Arctic. Results show that not only do beluga whales regularly interact with close kin, including close maternal kin, they also frequently associate with more distantly related and unrelated individuals. Findings will improve the understanding of why some species are social, how individuals learn from group members and how animal cultures emerge.

• Dinosaurs and flying pterosaurs may be known for their remarkable size, but a newly described species from Madagascar that lived around 237 million years ago suggests that they originated from extremely small ancestors. The fossil reptile, named Kongonaphon kely, or ‘tiny bug slayer’, would have stood just 10 centimetres tall. The description and analysis of this fossil and its relatives, published in the journal Proceedings of the National Academy of Sciences, may help explain the origins of flight in pterosaurs, the presence of ‘fuzz’ on the skin of both pterosaurs and dinosaurs, and other questions about these charismatic animals.

For more soundbites listen to Radio Mocha every Saturday at 7.30pm on Radju Malta and the following Monday at 9pm on Radju Malta 2. 

Did you know

• The National Gallery in London was only shut for two days during World War II, ironically during VE Day celebrations in May 1945.

• You are not imagining it ‒ summer heat really does make people crankier.

• The average garden snail has over 14,000 teeth.

• Leonardo da Vinci was notorious for missing deadlines. Pope Leo X is reported to have said: “This man will never accomplish anything, he thinks of the end before the beginning.”

• Eating too many carrots, pumpkin or citrus fruits can result in ‘carotenosis’, where your skin turns orange.

• There are more trees on Earth than there are stars in the Milky Way. Until now.

The post Dry- and wet-lab research – ‘Big data’ brings two worlds together appeared first on Artificial Intelligence.

To protect IoT devices and networks in smart building, smart city, healthcare, industrial and critical infrastructure environments against all types of advanced Gen VI cyber-threats, Check Point Software Technologies has introduced its Internet of Things (IoT) Protect solution. This new solution will deliver threat prevention and security management capabilities to block even unknown cyber-attacks at both IoT network and IoT device level, using threat intelligence and innovative IoT-specific security services.

“The number of IoT devices connected to the Internet continues to accelerate and will be 41.6 billion by 2025. Cyber criminals are targeting IoT devices across all industries including medical, industrial, smart building, smart office, so enterprises are making security a high priority. Given the huge volume and variety of IoT devices, organisations need an easy way to deploy security. Check Point’s comprehensive IoT Protect Security solution uses automation and threat intelligence to provide device risk assessment, network segmentation, and threat prevention from the most sophisticated cyber-attacks,” says Robyn Westervelt, IDC’s Research Director, Security & Trust.

As majority of organisations across the commercial, industrial, healthcare and utility sectors have deployed IoT and operational technology (OT) solutions, the cyber-risk has increased significantly as many IoT devices have vulnerabilities and cannot be patched, or use insecure communications protocols. In addition, organisations have diverse estates of devices from multiple vendors, with many shadow devices that are unmanaged and connected to networks without authorisation, so organisations have limited visibility and control of devices and their associated risks. Check Point’s IoT Protect will give network-level security and policy management together with Check Point security gateways, and IoT Protect Nano-Agents, which enable on-device runtime protection. The solution delivers Complete IoT device visibility and risk analysis that identifies and classifies IoT devices on any network through integrations with the leading discovery engines, to expose risks such as weak passwords, outdated firmware and known vulnerabilities, vulnerability mitigation and zero-day threat prevention even on unpatchable devices and intuitive Zero Trust network segmentation and management

To protect IoT devices and networks in smart building, smart city, healthcare, industrial and critical infrastructure environments against all types of advanced Gen VI cyber-threats, Check Point Software Technologies has introduced its Internet of Things (IoT) Protect solution. This new solution will deliver threat prevention and security management capabilities to block even unknown cyber-attacks at both IoT network and IoT device level, using threat intelligence and innovative IoT-specific security services.

“The number of IoT devices connected to the Internet continues to accelerate and will be 41.6 billion by 2025. Cyber criminals are targeting IoT devices across all industries including medical, industrial, smart building, smart office, so enterprises are making security a high priority. Given the huge volume and variety of IoT devices, organisations need an easy way to deploy security. Check Point’s comprehensive IoT Protect Security solution uses automation and threat intelligence to provide device risk assessment, network segmentation, and threat prevention from the most sophisticated cyber-attacks,” says Robyn Westervelt, IDC’s Research Director, Security & Trust.

As majority of organisations across the commercial, industrial, healthcare and utility sectors have deployed IoT and operational technology (OT) solutions, the cyber-risk has increased significantly as many IoT devices have vulnerabilities and cannot be patched, or use insecure communications protocols. In addition, organisations have diverse estates of devices from multiple vendors, with many shadow devices that are unmanaged and connected to networks without authorisation, so organisations have limited visibility and control of devices and their associated risks. Check Point’s IoT Protect will give network-level security and policy management together with Check Point security gateways, and IoT Protect Nano-Agents, which enable on-device runtime protection. The solution delivers Complete IoT device visibility and risk analysis that identifies and classifies IoT devices on any network through integrations with the leading discovery engines, to expose risks such as weak passwords, outdated firmware and known vulnerabilities, vulnerability mitigation and zero-day threat prevention even on unpatchable devices and intuitive Zero Trust network segmentation and management

IoT Protect supports over 1,600 IoT and operational technology (OT) protocols, applications and commands out of the box, and enables compliance best practices for regulations including HIPAA, NERC CIP, GDPR and many others. Its market-leading threat prevention capabilities are powered by Check Point ThreatCloud, the world’s largest and most powerful threat intelligence database. It maintains an open framework of technology partners specialising in the discovery and classification of IoT devices in a variety of verticals.

The post Check Point to protect IoT devices and networks against advanced cyber-attacks appeared first on Artificial Intelligence.

At the present scenario, video games portray a crucial role when it comes to AI and ML model development and evaluation. This methodology has been around the corner for a few decades now. The custom-built Nimrod digital computer by Ferranti introduced in 1951 is the first known example of AI in gaming that used the game nim and was used to demonstrate its mathematical capabilities.  

Currently, the gaming environments have been actively utilised for benchmarking AI agents due to their efficiency in the results. In one of our articles, we discussed how Japanese researchers used Mega Man 2 game to assess AI agents. Besides this, there are several popular instances where researchers used games to benchmark AI such as DeepMind’s AlphaGo to beat professional Go players, Libratus to beat pro players of Texas Hold’em Poker, among others.

In this article, let’s take a look at another simple video game called Snake and how machine learning algorithms can be implied to play this simple game.

Snake game is one of the classical video games that we all have played at least once in our childhood. In this game, the player controls the snake to maximise the score by eating apples that are spawned at random places. The snake will keep growing one grid every time the snake eats an apple. The only rule is that the snake has to avoid the collision in order to survive.

Researchers around the globe have been implementing various machine learning algorithms in this cult game. Below, we have mentioned a few implementations of neural network algorithms in the classic Snake game.

Snake Game Using Neural Networks & Genetic Algorithm

In a paper, researchers from the University of Technology, Poland used a neural network structure that decides what action to take from any given input. The Neural Network is called DNA by the researchers. The DNA class is the most important part of the snake as it is the “brain” that makes every decision. 

The class has matrixes with weights and separate ones with bias, which represent each layer of the neural network. The next step is followed by creating a function that allows calculating its performance, where the performance includes the number of moves the snake executed without dying and scores.

The Implementation

The researchers used neural networks with 1 hidden layer with 6 neurons and the genetic algorithm to find out which method and parameters are the best. At first, they randomly generated the population of snakes with an optimum size of 2000. Then they let the snakes play in order to understand how many steps were executed and how many apples the snake ate. 

This led to the calculation of fitness of each snake that helps to see which one performed the best and which one should have a higher probability of being chosen for breeding. For the Selection part, the researchers chose a pair of snakes (parents) that will give DNA to the new snake (child) where the probability of being chosen is based on fitness. After choosing the parents, the researchers crossover their DNA by taking some of the weights from the father and some from the mother and applying it to their child.

After selection, the next step is a mutation which follows when every new snake inherits the neural network from parents. Then the playing and mutation processes are repeated in order to get the best results.

Snake Game Using Deep Reinforcement Learning

In this research, the researchers develop a refined Deep Reinforcement Learning model to enable the autonomous agent to play the classical SnakeGame, whose constraint gets stricter as the game progresses. The researchers employed a convolutional neural network (CNN) trained with a variant of Q-learning.

Further, they proposed a designed reward mechanism to properly train the network, adopt a training gap strategy to temporarily bypass training after the location of the target changes, and introduce a dual experience replay method to categorise different experiences for better training efficacy. According to the researchers, the experimental results showed that the agent outperformed the baseline Deep Q-Learning Network (DQN) model and surpassed human-level performance in terms of both game scores and survival time in the Snake game.

The post HOW NEURAL NETWORK CAN BE TRAINED TO PLAY THE SNAKE GAME appeared first on Artificial Intelligence.

Currently, almost every company integrates Artificial Intelligence into its products, including deep learning. “The availability of current computing power, alongside labeled databases and trained networks that can be easily adapted for solving new problems – these are the factors that helped the deep learning field to finally grow”, said Roy Fahn, who leads the deep learning field at Systematics, the distributor of MATLAB software in Israel, and more.

Fahn gave a presentation at the Video Analytics Conference organized by iHLS for the seventh year. Elaborating on the advanced capabilities of deep learning, Fahn also presented the challenges in the field.

“One challenge is the labeling of the data – images, video, audio signals or other one-dimensional signals,” Fahn said. “Deep learning requires labeled data, and a lot of it, but nobody likes labeling data. It is also not simple to manage such huge quantities of data as required. Another problem is the use of powerful computing – not everyone knows how to operate GPU or has experience in sending tasks to the cloud. Another challenge – in many cases, it is hard to achieve an operating network after a first course of training.” 

“Another challenge is the difficulty to change a trained network and make it solve our own problems of interest. And finally – we don’t train networks in order to leave them in our computer but rather we want to download them to GPU, CPU, FPGA, etc., but not all of us have the time and know-how.”

Fahn is a graduate of the Technion Israel Institute of Technology, with second degree in Electrical Engineering and more than a decade of experience in the high-tech sector. Several years ago, he joined public activity at his home town, Petah Tikva, and for more than two years has been volunteering as a City Council member, including responsibility for the computing and information systems department.

At his presentation at the Conference, Fahn demonstrated how MATLAB software helps cope with all the challenges, turning deep learning into a more simple and accessible field.”Engineers and researchers need tools in order to leave them time for the important tasks,” he said in a special interview to iHLS, “the MATLAB environment is the right solution.”

How does MATLAB helps deal with deep learning challenges?

“Regarding data labeling, MATLAB environment offers tools that help mark objects in images through Bounding Box, or pixel-level labeling for Semantic Segmentation, or the labeling of audio or other one-dimensional signals – MATLAB environment includes friendly and powerful graphic interfaces that even enable the automation of the process.”

“For example, if an object was marked in certain video frame, it is possible to define a way to detect it in the next frames, thus label hundreds of images by just a few clicks. MATLAB interface can also offer possible markings of the objects in the images that were not yet labeled.”

“With MATLAB, it is very simple to manage the data. Special objects enable to refer all the hundreds and thousands of images or audio files as one entity, loading them through a single line of code, using another line of code to divide the database into sub-sets of training, validation, and testing.”

“Regarding the use of hardware to accelerate training and inference, MATLAB makes it very simple. In fact, GPU use is the default of the software.”

“Regarding visualization and debugging capabilities, this has always been the advantage of the MATLAB environment, and it hasn’t changed in the case of deep learning. MATLAB can present accuracy and loss graphs with the advance of the training, show the activations of the various layers in response to various entrances, create deep dream images, show the features, use techniques such as class activation mapping and occlusion sensitivity, prepare advanced confusion matrix, and can even enable to design the network using graphic interface.”

What are MATLAB’s advantages over Python and free deep learning environments?

According to Fahn, Python was not designed for technical computing, while MATLAB was engineered for it and has been fulfilling this role for already 36 years. “First of all, models developed in free environments can be transferred conveniently into MATLAB and vice versa. However, in the end of the day, the majority of engineers and researchers look for rapid solutions, and this is where MATLAB has substantial advantages, including its convenient labeling tools, data management capabilities, its excellent documentation that includes useful examples from various fields, and the simplicity of the hardware use for operations acceleration.”

“Just as MATLAB is faster than Python in technical computing, it is also more agile in the deep learning field, e.g. some training processes are four-times faster with MATLAB compared to with Python.”

What about the running time of the trained network over hardware?

“Through tools that convert MATLAB code to C++ code or CUDA it is currently possible to automatically download all the trained networks to Nvidia’s GPUs, Intel’s processors, ARM platforms, etc. and get an especially efficient code. Frames rate with MATLAB is double the one achieved through TensorFlow and PyTorch, as I demonstrated at our booth at the iHLS Video Analytics Conference and Exhibition.” 

So what about price considerations?

“Students at academic institutions enjoy free access to the software, and current prices for the industry are not high compared to past prices, and there are even special programs for startups. Finally, the value for money is a more rapid, simple and quality research or development process. Also from the learning possibilities – the company supplies a wide array of free instructions around the country and on the web.”

Finally, what to expect regarding MATLAB for deep learning in 2020?

“As annually, two major versions will be launched also in 2020, with many innovations in MATLAB in general, and in deep learning in particular, a field that will continue to enjoy special emphasis. After in the previous versions, advanced augmentation capabilities were added to the audio and image fields, a tool for Reinforcement Learning, automatic suggestion of optimal training parameters, custom layers building interface, and more – we are developing a capability that would enable automatic conversion of the trained networks from MATLAB to a code run on FPGA, a convenient interface for experiment management, deep learning for point clouds, etc.”

“There is certainly a reason why a few months ago, Gartner information technologies consultancy and research company marked the MATLAB environment as a leading software in the field, both from its current and future capabilities.”

The post The Ultimate Solution for Deep Learning Networks Development appeared first on Artificial Intelligence.

Recently San Francisco passed – in an 8-to-1 vote — a ban on local agencies to use facial recognition technologies. The move is likely not to be a one-off either. Other local governments are exploring similar prohibitions, so as to deal with the potential Orwellian risks that the technology may harm people’s privacy. “In the mad dash towards AI and analytics, we often turn a blind eye to their long-range societal implications which can lead to startling conclusions,” said Kon Leong, who is the CEO of ZL Technologies.

Yet some tech companies are getting proactive. For example, Microsoft has indicated there should be regulation of facial recognition systems (although, not a straight out ban). The company even declined a request to sell its own technology to a police department in California.

Keep in mind that — even with the strides in AI — there are still problems with the technology.  There are numerous cases where it has given off false positives.

“Before AI, the saying was: ‘Big brother is watching you, but he can’t see,’” said Stefan Ritter, who is the Chief Product Officer and co-Founder of Ruum. “In essence, it meant we had CCTV everywhere recording us, but in reality, it was not taking away our freedoms or rights — yes we were being taped, but in effect police only turned to the recordings when there was a crime severe enough to merit the many hours of painstakingly going through video recordings. However, with AI-powered facial recognition for social control, we could come dangerously close to a ‘Minority Report’-esque future, where neural networks could, in theory, recognize crimes before they happen. ‘Innocent until proven otherwise’ is one of the founding principles of the democratic state, so it’s crucial that we have a broad discussion about how we want to leverage AI in our social systems.”

Now of course, there are many benefits to facial recognition systems. They can be leveraged to identify diseases in MRIs or to even help predict crashes before they happen.

But then again, there really needs to be a focus on the unintended consequences. In fact, if there are high-profile mishaps, the result could be a stunting of AI’s progress.

“The San Francisco city government is setting a positive example by banning facial recognition technology,” said Asma Zubair, who is the Sr. Manager of IAST Product Management at Synopsys. “While it’s a good start, we must also recognize that the use of facial recognition in the private sector continues to grow. While the technology has improved greatly in recent years, there are known weaknesses when recognizing certain groups of people. As the adoption of facial recognition technology grows, raw video footage will become more easily available as structured data that includes biometric information and personally identifiable information will likely be stored in a searchable format — for example, names, location, time, date and so on. This structured data may be retained for periods of time which makes it susceptible to breaches and misuse. With so many data privacy breaches in the headlines already, organizations clearly aren’t ready to use facial recognition in a safe, secure, and responsible manner.”

The post SF Facial Recognition Ban: What Now For AI (Artificial Intelligence)? appeared first on Artificial Intelligence.

Big data and analytics are topics firmly embedded in our business dialogue. The amount of data we’re now generating is astonishing. Cisco predicts that annual global IP traffic will reach 3.3 ZB per year by 2021 and that the number of devices connected to IP networks will be more than three times the global population by 2021, while  Gartner predicts $2.5M per minute in IoT spending and 1M new IoT devices will be sold every hour by 2021. It’s testament to the speed with which digital connectivity is changing the lives of people all over the world.

Data has also evolved dramatically in recent years, in type, volume, and velocity – with its rapid evolution attributed to the widespread digitisation of business processes globally. Data has become the new business currency and its further rapid increase will be key to the transformation and growth of enterprises globally, and the advancement of employees, ‘the digital natives’.

The Cisco Global Cloud Index points to the Cloud as the top driver as exponential data centre growth with cloud centre traffic quadrupling in the next five years. Data generated by IoT applications (such as connected homes, smart cities and healthcare) will be 600ZB (zettabytes) per year by 2020, 39 times higher than current data centre traffic which is 15.3 ZB.

Big Data therefore has a far-reaching impact and meaning. But how do we understand it and its benefits, along with analytics on the journey to Digital Transformation? Understanding the value of Data is key to the successful implementation of operational strategies that facilitate agile and effective business growth.

Big data means better business 

Data is an enabler of future strategies and immediate change, thanks to the power of predictive analytics and advanced data science. Correctly harnessing data can help to achieve better, fact-based decision-making and improve the overall customer experience. By using new Big Data technologies, organisations can answer questions in seconds rather than days, and in days rather than months. This acceleration allows businesses to enable the type of quick reactions to key business questions and challenges that can build competitive advantage and improve performance, and provide answers for complex problems or questions that have resisted analysis.

Big Data and analytics are becoming closely intertwined and need to work together to deliver the promised results of Big Data. Traditionally, Data management and analytics have resided in different parts of the organisation. Breaking down organisational boundaries and creating better integration between the IT and business departments is a critical step on the road to successful transformation.

There is also a widespread realisation of the need for better Business Analytics (BI).Business Analytics are the skills, technologies, practices for continuous iterative exploration and investigation of past business performance to gain insight and drive business planning. The key is integrating Big Data with traditional Business Analytics to create a data ecosystem that allows the business to generate new insights while executing on what it already knows.

Keep learning. Skills are everything.

Proficiency with data mining and visualisation tools ranks as one of the most important skills in determining project success.

All organisations need to consistently develop new data mining skills to fully realise the business potential. A key trend in big data is machine learning. Big data experts who can harness machine learning technology to build and train predictive analytic apps such as classification, recommendation, and personalisation systems are in high demand. Statistical and Quantitative Analysis, which aims to understand or predict behaviour or events through the use of mathematical measurements and calculations, statistical modelling and research, is also imperative to accomplishment. Other key data mining techniques that are employed industry wide include:

• Association –  one of the best-known data mining techniques. With association, a pattern is discovered based on a relationship between items in the same transaction.
• Classification is a classic data mining technique based on machine learning.
• Clustering is a data mining technique that makes a meaningful or useful cluster of objects which have similar characteristics using the automatic technique.
• Prediction is one of a data mining techniques that discovers the relationship between independent variables and relationship between dependent and independent variables.
• Sequential patterns analysis seeks to discover or identify similar patterns, regular events or trends in transaction data over a business period.
• Decision tree technique; the root of the decision tree is a simple question or condition that has multiple answers.

Educate your stakeholders 

All stakeholders need to be educated and made aware of Data’s value and understand that it’s essential to business continuity and growth. But they may feel overwhelmed (and under informed) to the power and complexity of the data if it is not properly communicated and presented. Regular meetings, ideally face to face will enforce the importance of the issue and the need for their buy-in.

Deliver Digital Ready networks – it makes financial sense 

All today’s businesses must, via Network Function Virtualisation (decreasing the amount of proprietary hardware needed to launch and operate network services), and Software Defined Networking (that allows updates to be made in real time or as the business demands, in just a few clicks) deliver Digital Ready networks to gain competitive advantage.

The increased simplicity and reduced costs associated with deploying and maintaining a more digital-ready network are core benefits and therefore should be employed as a necessity to improve and enhance business efficiency.

Automation is a high priority 

Automation is a high priority in accelerating Digital Transformation, allowing organisations to optimise their existing processes. Automation technology is IT system and process agnostic, allowing businesses to build on their systems within the existing IT environment.

In order to create a transformative environment and improve speed and quality of delivery, organisations need to integrate automation into their existing processes to increase the ability to frequently release high-quality products –  and to enable revenue and profit growth.

Automation also improves operational efficiency and allows employees to focus on more rewarding tasks. With automation, cost-effective solutions are enabled for repetitive, rules-based tasks. In addition, the prospect of human error is eliminated, delivering outcomes that are 100% accurate. By automating tasks, companies can significantly reduce the overall process cycle.

The road towards digital transformation is a business critical one. Organisations embarking on this journey need to consider how each aspect of their business can be optimised to fulfil new digital objectives and new growth potential.  Big data and analytics play a pivotal role in digital transformation, enabling organisations to optimise their existing processes and stay ahead of the competition.

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