Author: Data Science

Every passing day more and more organizations are investing more and more resources in acquiring a stronger analytics strategy for handling their big-data. Certainly a great move if done right. So, when everyone is focusing on analytics, why not start thinking about building it as a competitive advantage?

In a recent research conducted – 96% – said that analytics will become more important to their organizations in the next three years, according to a Deloitte report based on a mix of 100 online surveys and 35 interviews conducted with senior executives at 35 companies in North America, the UK and Asia.

With increased hype around big-data, analytics is already a crucial resource in almost every company but it has still lot of room for improvements. Like every new technology, lot of communication channels needs to be established, loads of buy-ins needs to happen and charter needs to be approved and budgeted. But world is moving there. So, why not rethink about the current analytics strategy and build it to provide a sustained competitive edge over the long term to help business stay competitive and ahead of the curve against other competitors?

No, it’s not a rocket science nor it is extremely difficult to do. All it requires is a roadmap, consistent deployment and frequent iterative executions. As a roadmap consider following 5 steps to pursue the organization to the road of analytics driven competitive edge.

Acquire the resources: Getting the magic team, the infrastructure, partnerships, and right relationships to get going on data analytics journey is of utmost importance. As the big-data hype is hitting the market, resources are in great demand, so securing the right mix that could build your analytics business is important and every effort should be made to make sure you acquire right resources. Getting an early head start will go a long way in ensuring timely delivery of analytics framework when the business needs the most. How those resources needs to be acquired is another topic for another blog, but getting yourself prepared to hire is important.

Build analytics across business verticals: Once you have right resources needed to build analytics framework, next step is to make sure analytics frameworks encapsulates various business verticals. Financial department is most used to having some analytics platform. Other departments like marketing, customer, sales, & operations should also be used for capturing data that will further be analyzed for more insights and findings to help business understand the data and insights mores. Not necessarily the more captured data translates to better insights, the quality of data plays an important role. But at start it is difficult to find out the quality of data, so in case of missing information on importance of data fields, the more data we capture the better we get at covering the most of the business through analytics.

Utilize analytics for decision-making: Getting good data and better insights does not necessarily translate to better decisions. The processes need to be deployed that could leverage insights through analytics and account them into decisions made by businesses. A data backed decision tends to work with more accuracy than without it. Decision quality increases with more quantified backings. Analytics driven decision-makings should be done at not only the high business levels but also at the level of front-line workers dealing with customers directly.

Coordinate and align analytics: Another important thing to note here is that often times analytics does not have a clear head or vertical that causes quality issues, lack of ownership and inefficiencies in the system. There needs to be a better coordination charter built and approved to make sure all the stakeholders and processes contribute to analytics framework for its effective functioning. This should also include a clear communication of roles and interactions across various groups and approved processes.

Create a long term strategy: With right infrastructure, laid out communication charter, well managed processes & right utilization of findings the businesses will start using analytics proactively to help create a competitive edge based on findings. One thing that needs to be considered is building it for a long-term strategy. Having a long-term strategy will provide a consistent and sustained roadmap for strategy execution. It will also spread some light on how to view data on long term basis and how to better shape it for faster learning and effective analytics.

So, covering for all the above stated 5 steps will provide for a framework that energizes organizations with state of the art competitive edge that is based on core data and analytics. If done effectively, the data & it’s analysis will provide for an effective learning system that sustainably provides relevant insights in real-time for faster execution.

Source: Building Big Analytics as a Sustainable Competitive Advantage

In machine learning, statistics play a significant role in achieving data distribution and the study of inferential statistics. A data scientist must understand the math behind sample data and Central Limit Theorem answers most of the problems. Let us discuss the concept of the Central Limit Theorem. It assumes that the distribution in the sample […]

The post An Introduction to Central Limit Theorem appeared first on GreatLearning.

Source

Advantages of a Truly Open-Access Data-Sharing Model: Traditionally, data from clinical trials has been siloed, but now there is support for making such data open for all to use. Project Data Sphere is one such effort.

Detecting mutations could lead to earlier liver cancer diagnosis: Aflatoxin induces a mutation that can cause liver cancer. Now, MIT researchers have developed a method to detect this mutation before cancer develops.

Australia launches machine-learning centre to decrypt the personal genome: Geneticists and computer scientists have launched the Garvan-Deakin Program in Advanced Genomic Investigation (PAGI). They hope to work out the complex genetic causes of diseases.

More genomic sequencing announced for Victoria: Selected Australian patients will have access to genomic sequencing. This project is intended to help track drug-resistant “superbugs” as well as 4 other personalized conditions.

No, We Can’t Say Whether Cancer Is Mostly Bad Luck: Last week’s news on the mutations that cause cancer is disputed among cancer scientists.

Source: April 3, 2017 Health and Biotech analytics news roundup by pstein

vSphere 6.5 offers a resilient, highly available, on-demand infrastructure that is the perfect groundwork for any cloud environment. It provides innovation that will assist digital transformation for the business and make the job of the IT administrator simpler. This means that most of their time will be freed up so that they can carry out more innovations instead of maintaining the status quo. Furthermore, vSpehere is the foundation of the hybrid cloud strategy of VMware and is necessary for cross-cloud architectures. Here are essential features of the new and updated vSphere.

vCenter Server appliance

vCenter is an essential backend tool that controls the virtual infrastructure of VMware. vCenter 6.5 has lots of innovative upgraded features. It has a migration tool that aids in shifting from vSphere 5.5 or 6.0 to vSphere 6.5. The vCenter Server appliance also includes the VMware Update Manager that eliminates the need for restarting external VM tasks or using pesky plugins.

vSphere client

In the past, the front-end client that was used for accessing the vCenter Server was quite old-fashioned and stocky. The vSphere has undergone necessary HTML5 alterations. Aside from the foreseeable performance upgrades, the change also makes this tool cross-browser compatible and more mobile-friendly.  The plugins are no longer needed and the UI has been switched for a more cutting-edge aesthetics founded on the VMware Clarity UI.

Backup and restore

The backup and restore capabilities of the VSpher 6.5 is an excellent functionality that enables clients to back up data on any Platform Services Controller appliances or the vCenter Server directly from the Application Programming Interface(API) or Virtual Appliance Management Interface (VAMI). In addition, it is able to back up both VUM and Auto Deploy implanted within the appliance. This backup mainly consists of files that need to be streamed into a preferred storage device through SCP, FTP(s), or HTTP(s) protocols.

Superior automation capabilities

With regards to automation, VMware vSphere 6.5 works perfectly because of the new upgrades. The new PowerCLI tweak has been an excellent addition to the VMware part because it is completely module-based and the APIs are at present in very high demand. This feature enables the IT administrators to entirely computerize tasks down to the virtual machine level.

 Secure boot

The secure boot element of vSphere comprises the -enabled virtual machines. This feature is available in both Linux and Windows VMs and it allows secure boot to be completed through the clicking of a simplified checkbox situated in the VM properties. After it is enabled, only the properly signed VMs can utilize the virtual environment for booting.

 Improved auditing

The Vsphere 6.5 offers clients improved audit-quality logging characteristics. This aids in accessing more forensic details about user actions. With this feature, it is easier to determine what was done, when, by whom, and if any investigations are essential with regards to anomalies and security threats.

VMware’s vSphere developed out of complexity and necessity of expanding the virtualization market. The earlier serve products were not robust enough to deal with the increasing demands of IT departments. As businesses invested in virtualization, they had to consolidate and simplify their physical server farms into virtualized ones and this triggered the need for virtual infrastructure. With these VSphere 6.5 features in mind, you can unleash its full potential and usage. Make the switch today to the new and innovative VMware VSphere 6.5.

Source by thomassujain

Changes in business and technology in the financial services industry have opened up new possibilities. With the rapidly growing number of customer interactions through digital banking, there is a huge volume of customer data now available that can provide strategic opportunities for business growth and tremendous prospects for improved management tools.

Originally posted via “The power of data in the financial services industry”

Source: The power of data in the financial services industry by analyticsweekpick

The heterogeneous complexities of big data present the foremost challenge in delivering that data to the end users who need them most. Those complexities are characterized by:

• Disparate data sources: The influx of big data multiplied the sheer amount of data sources almost exponentially, including those both external and internal ones. Moreover, the quantity of sources required today are made more complex by…
• Multiple technologies powering those sources: For almost every instance in which SQL is still deployed, there is seemingly another application, use case, or data source which involves an assortment of alternative technologies. Moreover, accounting for the plethora of technologies in use today is frequently aggravated by contemporary…
• Architecture and infrastructure complications: With numerous advantages for deployments in the cloud, on-premise, and in hybrid manifestations of the two, contemporary enterprise architecture and infrastructure is increasingly ensnared in a process which protracts time to value for accessing data. The dilatory nature of this reality is only worsened in the wake of…
• Heightened expectations for data: As data becomes ever entrenched in the personal lives of business users, the traditional lengthy periods of business intelligence and data insight are becoming less tolerable. According to Dremio Chief Marketing Officer Kelly Stirman, “In our personal lives, when we want to use data to answer questions, it’s just a few seconds away on Google…And then you get to work, and your experience is nothing like that. If you want to answer a question or want some piece of data, it’s a multi-week or multi-month process, and you have to ask IT for things. It’s frustrating as well.”

However, a number of recent developments have taken place within the ever-shifting data landscape to substantially accelerate self-service BI and certain aspects of data science. The end result is that despite the variegated factors characterizing today’s big data environments, “for a user, all of my data looks like it’s in a single high performance relational database,” Stirman revealed. “That’s exactly what every analytical tool was designed for. But behind the scenes, your data’s spread across hundreds of different systems and dozens of different technologies.”

Avoiding ETL

Conventional BI platforms were routinely hampered by the ETL process, a prerequisite for both integrating and loading data into tools with schema at variance with that of source systems. The ETL process was significant for three reasons. It was the traditional way of transforming data for application consumption. It was typically the part of the analytics process which absorbed a significant amount of time—and skill—because it required the manual writing of code. Furthermore, it resulted in multiple copies of data which could be extremely costly to organizations. Stirman observed that, “Each time you need a different set of transformations you’re making a different copy of the data. A big financial services institution that we spoke to recently said that on average they have eight copies of every piece of data, and that consumes about 40 percent of their entire IT budget which is over a billion dollars.” ETL is one of the facets of the data engineering process which monopolizes the time and resources of data scientists, who are frequently tasked with transforming data prior to leveraging them.

Modern self-service BI platforms eschew ETL with automated mechanisms that provide virtual (instead of physical) copies of data for transformation. Thus, each subsequent transformation is applied to the virtual replication of the data with swift in-memory technologies that not only accelerate the process, but eliminate the need to dedicate resources to physical copies. “We use a distributed process that can run on thousands of servers and take advantage of the aggregate RAM across thousands of servers,” Stirman said. “We can execute these transformations dynamically and give you a great high-performance experience on the data, even though we’re transforming it on the fly.” End users can enact this process visually without involving script.
Reflections

Today’s self-service BI and data science platforms have also expedited time to insight by making data more available than traditional solutions did. Virtual replications of datasets are useful in this regard because they are stored in the underlying BI solution—instead of in the actual source of data. Thus, these platforms can access that data without retrieving them from the initial data source and incurring the intrinsic delays associated with architectural complexities or slow source systems. According to Stirman, the more of these “copies of the data in a highly optimized format” such a self-service BI or data science solution has, the faster it is at retrieving relevant data for a query. Stirman noted this approach is similar to one used by Google, in which there are not only copies of web pages available but also “all these different ways of structuring data about the data, so when you ask a question they can give you an answer very quickly.” Self-service analytics solutions which optimize their data copies in this manner produce the same effect.

Prioritizing SQL

Competitive platforms in this space are able to account for the multiplicity of technologies the enterprise has to contend with in a holistic fashion. Furthermore, they’re able to do so by continuing to prioritize SQL as the preferred query language which is rewritten into the language relevant to the source data’s technology—even when it isn’t SQL. By rewriting SQL into the query language of the host of non-relational technology options, users effectively have “a single, unified future-proof way to query any data source,” Stirman said. Thus, they can effectively query any data source without understanding its technology or its query language, because the self-service BI platform does. In those instances in which “those sources have something you can’t express in SQL, we augment those capabilities with our distributed execution engine,” Stirman remarked.
User Experience

The crux of self-service platforms for BI and data science is that by eschewing ETL for quicker versions of transformation, leveraging in-memory technologies to access virtual copies of data, and re-writing queries from non-relational technologies into familiar relational ones, users can rely on their tool of choice for analytics. Business end users can choose from any popular Tableau, Qlik, or any other preferred tool, while data scientists can use R, Python, or any other popular data science platform. The fact that these solutions are able to facilitate these advantages at scale and in cloud environments adds to their viability. Consequently, “You log in as a consumer of data and you can see the data, and you can shape it the way you want to yourself without being able to program, without knowing these low level IT skills, and you get the data the way you want it through a powerful self-service model instead of asking IT to do it for you,” Stirman said. “That’s a fundamentally very different approach from the traditional approach.”

Source by jelaniharper

One of the most common use cases of artificial intelligence at the moment is its ability to handle massive datasets, processing and interpreting them. A task that human data analysts would take ages to complete, if at all, is performed in no time, and without the possibility of human error. At the same time, the average person creates an increasingly larger digital footprint, leaving a trace in the form of a vast amount of personal information on the internet.

Corporations and governments, then, gather, store, and feed that information to powerful AI algorithms in order to learn as much as possible about that person for marketing (and other) purposes. All this has led to heated debates over the safety of our personal data and its potential misuse.

No doubt AI holds tremendous potential to disrupt and improve our lives, but there are some hidden traps and pitfalls that have to be discussed and overcome.

Is There Such a Thing as Too Much Data?

It depends on the point of view. Brands seem to need every single bit of information on their target audience in order to better understand their needs and preferences so that they can tailor the right marketing message.

While that’s in a way a legitimate thing, the rise of advanced technologies, including AI, has led this thirst for information to get in the way of their customers’ privacy.

Namely, before AI and big data analytics, it was impossible to properly interpret unstructured data coming from different sources and in different formats, which left a big chunk of information uninterpretable and thus unused.

But, once the technologies managed to crack this code and translate illegible data into the actual information, the concept of digital privacy became an issue.

In 2012, an incident showed how intimidatingly accurate data analytics can be, and what that means for an ordinary user. In an attempt to assist its customers in finding everything they might need, Target sent coupons for cribs and baby clothes to a high school girl through the mail. Her unsuspecting father went to complain, only to find out that this wasn’t just a random mistake – the store’s algorithm picked up different cues based on what kind of products the pregnant girl purchased and viewed.

Similarly, it’s possible to track and locate people with the help of their own mobile devices and wearables, which means that it’s virtually impossible to go off the radar and seclude oneself.

Voice and facial recognition additionally complicate things as these technologies are capable of completely obliterating anonymity in public places.

Although it’s somewhat comforting to know that this way many wrongdoings and crimes can be prevented and identified, the lack of regulations might put us all under surveillance. Besides, there are growing fears of misidentification and wrongful convictions. According to research studies, this technology isn’t accurate when it comes to identifying people of color, which can have grave consequences.

The Concept of Privacy in a Digital Age

The Facebook-Cambridge Analytica Scandal was just one in line of numerous incidents that demonstrated how unprotected our data is and how easy it is to obtain it, with almost no repercussions.

Just 20 years ago privacy was still a concept that existed only in the offline, physical world. And it was much easier to protect yourself and your personal data by not disclosing your credit card or Social Security number.

Today, as we use numerous online services, it’s hard to keep your data to yourself. If you want to purchase something online, you’ll have to provide your credit card number and authorize the transaction. Websites store this sensitive information online, and a single hacker attack can expose it.

For example, the data of up to 500 million Marriott International guests was compromised in a data breach in 2018.

But, it’s not only hackers and cybercriminals that jeopardize our privacy.

It’s not a secret that many companies use social media and the internet to find out more about their potential and existing employees. This can have severe implications, as people can be (and usually are) held accountable for what they post online. Some have even lost their jobs due to certain online activities like posting a profanity-laced tweet, which is exactly what happened to a NASA intern.

Is There a Solution to This Issue?

It can’t be denied that being constantly monitored and under surveillance can be frustrating.

But it would be a shame to curb the development of such immense technological advancement because of unresolved privacy issues.

AI, big data analytics, IoT, and 5G, for example, are much maligned in some circles due to the fact that they heavily rely on gargantuan amounts of data as well as that they enable a massive network of interconnected devices that can be controlled remotely.

What does this mean?

It can be both a gigantic blessing and a curse. When combined, these technologies allow, for example, the possibility of remote surgery that could save millions of lives. Similarly, IoT is a network that enables remote control of cars, homes, and appliances.

On the other hand, the data generated by these technologies can be compromised or used for harmful purposes.

Another example is AI-powered chatbots that have become indispensable in numerous industries, thanks to the fact that they can improve customer engagement and juggle multiple customer queries at the same time. This way, they help customers and increase satisfaction.

They are also capable of collecting, analyzing, and storing customer information in order to personalize every subsequent customer touchpoint and offer the best and most personalized service. This way, companies can reduce operational costs and boost customer retention rates.

Luckily, there are ways to make the most of all these AI benefits but not at the sake of compromising users’ privacy.

A New Dawn of Digital Privacy

How are we going to achieve this win-win situation and give brands our data without any fears of it being misused?

The trick is in combining cryptography and machine learning, which will result in AI’s ability to learn from data without actually seeing it.

This way, the privacy of end-users will be protected, and at the same time, companies will be able to leverage their data without breaking any laws of ethics.

Several technologies will make this happen:

• Federated learning: This concept describes a decentralized AI framework distributed across millions of devices. Federated learning will enable scientists to create, train, improve, and assess a shared prediction model while keeping all the data on the device. In a nutshell, companies won’t have access to users’ raw data as well as no possibility of labeling it. This technology is a synergy of AI, blockchain, and IoT, keeps users’ privacy safe, and yet provides all the benefits of aggregated model improvement.
• Differential privacy: A number of applications, including maps or fitness and health apps, collect individual users’ data so that they can make traffic predictions and or analyze users’ fitness levels and other parameters. At the moment, it’s theoretically possible to match individual contributors and their data. Differential privacy will add some randomness to the entire procedure and make it impossible to trace back the information. As a result, it won’t be possible to expose the identity of individual contributors, while allowing for their data to be collected and analyzed.
• Homomorphic encryption: This technology uses machine learning algorithms to process and analyze encrypted data without accessing sensitive information. This data is encrypted and analyzed on a remote system. The results are sent in an encrypted form too, and they can be unlocked only by using a unique key so that the privacy of users whose data is being analyzed can be protected.

We’re still far from finding the right solution to the problem of privacy, but these small steps help in keeping things under control. AI and other technologies keep on evolving, which means that other obstacles will emerge, meaning that scientists and security experts will have to keep pace and constantly upgrade security protocols.

The post AI and Privacy: What’s in store for the the future? appeared first on Big Data Made Simple.

Source: AI and Privacy: What’s in store for the the future? by administrator

This free 1-hour webinar from GigaOm Research brings together experts in Cloud Computing, featuring GigaOm analyst David Linthicum and special guest from Intel Ananth Sankaranarayanan. The discussion will focus on understanding the future of cloud service providers as platforms for innovation, including changing-enabling technology, and how to prepare.

So what will be important to cloud services providers next year, two years, or five years? It’s a changing game where those that are most innovative, and leading the market, carry the day. So, what will be the focus?

This webinar takes an in-depth look at what’s next for cloud service providers, and those that consume cloud services. We’ll address obvious trends, such as artificial intelligence (AI) and machine learning (ML), use of containers, growth of 5G, serverless options, as well as new trends and approaches yet to arrive on the technology radar screen.

In this 1-hour webinar, key questions will be answered:

• How existing hype-driven trends are reflective of cloud server provider services, including how ML, use of containers, and serverless will likely grow in the future?
• Where are the AI use cases moving, and how CSPs can proactively keep up?
• How cloud service providers are learning to work and play well together, including hosting multi-cloud tools and technology?
• How cloud infrastructure is changing, and how you can stay ahead of the resource consumption curve?
• What aspects of AI should CSPs focus on now, and how CSPs can win a multi-billion dollar market?
• How Edge and 5G is changing the game, and why you’re already behind?
• The role of Intel in providing the “power to the clouds,” including key technology to accelerate the reach and capabilities of CSPs today?
• Where does open source play? Where should you place your bets now?

Join us to discover what CSPs need to know now, explore what the public cloud will be like in both the short and long term, and learn where to invest your time today, as well as in the future.

Register now to join GigaOm and Intel for this free expert webinar.

Who Should Attend:

• Chief Architects or Chief Engineers
• Product or Solution Architecture Leaders
• Enterprise, Solutions, Infrastructure or Cloud Architects
• Software or Infrastructure Engineers
• Product Management Leaders

Source: Seizing AI and the Edge for Cloud Service Providers (CSPs) by analyticsweekpick

Bajaj Allianz Life Insurance Company recently hosted a unique insurance summit focused on putting customer experience first. The event titled  “Future Perfect -Customer First Insurance Industry Summit 2018” was a full day event saw attendance by 21 insurance companies out of the 24 carriers that operate in India. While the focus was life insurance, the summit also saw participation from a few general insurance carriers.

The central theme of the summit was building and optimizing processes that would keep customer experience at the forefront of every customer interaction and transaction. In line with theme, experts from the insurance industry, regulatory bodies and cyber crime divisions of the Police spoke about upcoming trends in the insurance industry, fraud mitigation using advanced analytics, fighting cyber fraud etc…

Dr. Karnik, Chief Data Scientist at Aureus, spoke about how artificial intelligence and machine learning are being used to predict, and prevent fraud in the insurance industry. His presentation was based on his experience designing predictive models for early claims and fraud prevention across some of the largest life insurance carriers.

 

The complete presentation can be viewed below.

Using Machine Learning to Find a Needle in a Haystack by Dr. Nilesh Karnik

 

Dr. Karnik’s talk was very well received as  it derived from learnings from real life scenarios.  The complete presentation can be downloaded from here.

 

Future Perfect -Customer First Insurance Industry Summit 2018 put into focus the immediate challenges that the insurance industry as a whole is facing. The perspectives from multiple stakeholders – regulators, carriers, technology partners and cyber teams will help shape the next best action to make the end consumer experience epic and safe.

Originally Posted at: Building a Customer First Insurance Experience

Awesome, not awesome.

#Awesome
“…In theory, AIs could be used to predict the evolution of the coronavirus too. Inam imagines running unsupervised learning algorithms to simulate all possible evolution paths. You could then add potential vaccines to the mix and see if the viruses mutate to develop resistance. “This will allow virologists to be a few steps ahead of the viruses and create vaccines in case any of these doomsday mutations occur,” he says. It’s an exciting possibility, but a far-off one. We don’t yet have enough information about how the virus mutates to be able to simulate it this time around.” — Will Douglas Heaven, Editor Learn More from MIT Technology Review >

#Not Awesome
“Momentarily put aside your positions on immigration policy, if you will, and consider this case of alleged algorithmic rigging. Whatever your feelings on people seeking legal status in the US, you may find cause for concern about humanity’s growing reliance on machines to determine liberty. Last week, the Bronx Defenders and New York Civil Liberties Union filed a complaint in New York federal district court against local Immigration and Customs Enforcement (ICE) authorities. They allege that the agency adjusted the algorithm it uses to decide when someone should be released on bond. Now, detainees being held on civil immigration offenses overwhelmingly remain in custody even when they pose no flight or public safety risk and regardless of medical conditions.” — Ephrat Livni, Reporter Learn More from Quartz >

What we’re reading.

1/ A team of scientists uses a deep learning algorithm to discover an antibiotic that fights against drug-resistant bacteria in an unconventional way. Learn More from Quanta Magazine >

2/ Now that 700,000 people around the world die each year from infections that were formerly treated by antibiotics, scientists have a moonshot goal of using AI to create “resistance-proof” antibiotics. Learn More from The Atlantic >

3/ As we can use more and more data to predict human behavior, shows like Westworld and Devs try to show us what it will look like as free will erodes. Learn More from The Atlantic >

4/ Many executives are paying for AI tools that eventually need be duct-tapped together with other tools before they’ll ever be useful for their organization. Learn More from Harvard Business Review >

5/ Some AI algorithms can increase bias in the workplace, but a new company is finding ways to use algorithms that nudge people in ways that will decrease bias. Learn More from The New York Times >

6/ To better prepare students for a workplace in which roles are augmented by AI algorithms, business schools begin focusing coursework on topics like ethics, leadership, and emotional intelligence. Learn More from Knowledge @ Wharton >

7/ Google releases an open source tool to help developers build quantum machine learning algorithms that can be duplicated and used by others. Learn More from MIT Technology Review >

Links from the community.

“google-research/automl_zero/” submitted by Samiur Rahman (@samiur1204). Learn More from GitHub >

“20 women doing fascinating work in AI, machine learning and data science” submitted by Avi Eisenberger (@aeisenberger). Learn More from Silicon Republic >

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Source: The future of viruses and AI