Friday, 29 March 2019

Mobile Visions: IBM’s Plans for AI, Cloud Computing, 5G Networks

By Berge Ayvazian, Senior Analyst and Consultant at Wireless 20/20

I attended the Mobile World Congress in Barcelona last month to learn more about how the wireless industry is leveraging AI and intelligent cloud technologies on the road to deploying 5G networks.  Among the many software and hardware companies leading the AI revolution, I was most impressed by IBM which has aligned with its major operator clients and partners to showcase the cloud and AI technologies that will underpin the 5G era.

IBM is uniquely positioned to help companies bridge their on-premises systems to different cloud platforms and vendors so that they can manage data, workloads, and processes across the breadth of their IT systems—from the enterprise, to the cloud, to the device, to the edge of the network.  The recently-acquired Red Hat is now at the heart of IBM’s hybrid cloud strategy and the company’s push to make Watson available anywhere.

IBM has also established partnerships with leading telecom companies like Vodafone to help companies like KONE, Volkswagen’s SEAT, and Japanese textile manufacturer, Mitsufuji deliver on the promise of “Intelligent Connectivity”.

Convergence of AI and Cloud Computing in 5G Wireless Networks

The convergence of 5G, cloud computing, and artificial intelligence has begun to transform businesses around the world.   Cloud and high-speed connectivity help companies tap a plethora of powerful IT services such as AI, augmented reality, and blockchain providing highly-contextualized and personalized experiences for customers and employees.  However, getting everything to work together in a secure and optimized way is a major obstacle. The average enterprise using up to 15 clouds and multiple types of AI.

IBM and Vodafone last month signed a landmark $550M partnership to address this challenge by bringing together connectivity, cloud, and AI technologies to help companies remove complexity from their digital transformations. This new venture leverages the combination of Vodafone and IBM capabilities to address the “full stack” of real-world multi-cloud concerns in all layers of their organizations.   Under this agreement, IBM will supply Vodafone Business with managed services in the areas of cloud and hosting.  Vodafone Business and IBM will also together work to build and deliver solutions in areas like AI, cloud, 5G, IoT, and software-defined networking to enterprise customers. Vodafone Business and IBM will help to drive innovation and transform user experiences by providing the open cloud, connectivity, and portable AI technologies that companies need to manage data, workloads, and processes across the breadth of their IT systems across multiple industries—from retail to agriculture.

5G and AI Transform Urban Mobility, Proactive Maintenance and Worker Safety solutions

The Spanish carmaker SEAT, a member of the Volkswagen Group since 1986, launched “Mobility Advisor” proof of concept powered by Watson AI running on the IBM Cloud.  This new solution is part of SEAT’s “micromobility” strategy to provide personalized and eco-friendly choices for drivers—from the car to the eKickScooter designed to transform driving in cities.  SEAT presented its concept car Minimó, promising to revolutionize urban mobility, integrating Mobility Advisor, which uses IBM Watson AI to help citizens make informed decisions about their daily transportation options: from cars, to scooters, bikes, and public transport.  Mobility Advisor uses IBM Watson Assistant to learn a user’s preferences and make personalized recommendations for how best to complete a journey.  The SEAT Mobility Advisor is Connected to the IBM Cloud and designed to run as a mobile app on 4G/5G networks to provide users with a conversational interface to plan, suggest the most suitable transportation options and optimize routes taking into account weather forecasts, traffic reports, and things happening in the city that day.  With the roll-out of 5G networks in cities in the coming years, the possibilities for transforming the driver experience are limitless.

Ultimately, 5G and AI can also transform IoT, the factory floor, proactive maintenance, and worker safety solutions.  Using IBM IoT and Cloud technology, KONE, a leading manufacturer of elevators and escalators, is moving from reactive to proactive to predictive services to keep more than one billion people around the world on the move every day. Working with IBM IoT technology, KONE can constantly monitor up to 200 critical parameters of a single piece of equipment, achieve a 25% improvement in first time fix and bring a totally new experience to maintenance services.

IBM also announced a partnership with Mitsufuji, a Japanese company that provides worker safety solutions including connected garments, IoT technology and AI to alert workers to high-risk situations. Mitsufuji is using IBM’s Maximo Worker Insights as a scalable SaaS solution along with IBM’s IoT Platform, advanced analytics and cloud capabilities, to help organizations monitor worker safety, spot trends and potential problems, and react to changing environmental conditions.

After a week at MWC19, and briefings with leading mobile operators and major vendors such as IBM, I am in a better position to better understand the potential ROI from AI, cloud computing and 5G wireless networks.

Thursday, 28 March 2019

Blockchain and IoT: A Merger that Guarantees Valid and Secure Transactions

IoT (Internet of Things) is increasingly creating new opportunities for businesses as well as markets. And the technologies that created IoT is not changing the Internet, but everything that is connected to the Internet.

In fact, IoT connected devices are generating so much data that the question on technology leaders’ mind is - How to make all this data secure & reliable?

Although a huge amount of research is still required, it is believed that IoT’s flaws, in terms of security, can be fixed by merging Blockchain with IoT.

Blockchain is, without any doubt, the safest way to encrypt & secure data. Blending blockchain with IoT can provide the highest-level of security protocols, especially for data as well as a transaction process.

How Can Blockchain Level-Up IoT Security?

First of all, Blockchain is highly secure and impossible to manipulate by anyone connected to the blockchain network. Ever since Bitcoin, blockchain development has come a long way and can now store anything of value.

So why not save data in it?

Blockchain is the perfect structure to form the foundation of secure IoT Data Solutions.

For example, the present IoT devices rely on client-service architecture for data storage. This means, All devices are first recognized, authenticated, and connected through ...


Read More on Datafloq

Outreachy 2018-2019 In Review

Over the past three months, I have been mentoring two Outreachy interns, David and Latha, with my co-mentor, Jeff Thompson. Our project was to introduce a standardized way for creating an audit log of Jenkins and plugins using Apache Log4j Audit. While this type of feature is addressed by other existing plugins, there is no unifying way for plugins to contribute their own actions. This project provided ample opportunities for each of our interns to experience the community processes for starting a new Jenkins plugin, contributing changes to Jenkins itself in order to support more audit event types, using CICD principles, and developing a Jenkins Enhancement Proposal to begin the standardization process of audit logging throughout the ecosystem.

During this internship, David and Latha contributed several aspects of the project, much of which lays the foundation for easily instrumenting more subsystems and plugins with audit logs. A template log4j2.xml file is used for allowing more complex logging output configurations with a configuration UI.

Audit log configuration UI

New APIs have been introduced in Jenkins to allow for more authentication-related events to be audited by the plugin. Audit events have been defined for a few authorization scenarios and some build events. For example, here is a snippet of audit log output for a build execution in the JSON layout:

{
  "thread" : "Executor #0 for master : executing test #1",
  "level" : "OFF",
  "loggerName" : "AuditLogger",
  "marker" : {
    "name" : "Audit",
    "parents" : [ {
      "name" : "EVENT"
    } ]
  },
  "message" : "Audit [buildStart buildNumber=\"1\" cause=\"[Started by user anonymous]\" projectName=\"test\" timestamp=\"Mon Mar 25 13:48:09 CDT 2019\" userId=\"SYSTEM\"]",
  "endOfBatch" : false,
  "loggerFqcn" : "org.apache.logging.log4j.audit.AuditLogger",
  "instant" : {
    "epochSecond" : 1553539689,
    "nanoOfSecond" : 810000000
  },
  "contextMap" : { },
  "threadId" : 54,
  "threadPriority" : 5
}
{
  "thread" : "Executor #0 for master : executing test #1",
  "level" : "OFF",
  "loggerName" : "AuditLogger",
  "marker" : {
    "name" : "Audit",
    "parents" : [ {
      "name" : "EVENT"
    } ]
  },
  "message" : "Audit [buildFinish buildNumber=\"1\" cause=\"[Started by user anonymous]\" projectName=\"test\" timestamp=\"Mon Mar 25 13:48:10 CDT 2019\" userId=\"SYSTEM\"]",
  "endOfBatch" : false,
  "loggerFqcn" : "org.apache.logging.log4j.audit.AuditLogger",
  "instant" : {
    "epochSecond" : 1553539690,
    "nanoOfSecond" : 155000000
  },
  "contextMap" : { },
  "threadId" : 54,
  "threadPriority" : 5
}

Best of all, this project has helped instill important software engineering values such as automated testing and continuous delivery.

As we conclude this round, we look forward to participating in the next Outreachy internship to continue this project and grow the community. For more information about the next round, check out the Outreachy website.

Wipro joins hands with IIT Kharagpur for research on 5G, AI

Wipro has joined hands with Indian Institute of Technology, Kharagpur to jointly carry out applied research on 5G and artificial intelligence

India's case on cryptocurrency ban lacks transparency: Experts

The global market for cryptocurrency was valued at $574.3 million in the year 2017 and is expected to be worth $6702.1 million by the end of 2025.

Control Your Data: How Endpoint Security Prevents Third-Party Breaches

There’s a dark side to outsourcing business services.

Beyond the advantages of cost efficiency and flexibility, outsourcing comes with inherent security risks. Investing heavily in securing your in-house network isn’t enough. One misstep when working with a third-party service provider can give attackers direct access to your most sensitive data regardless of your perimeter defenses.

According to a 2018 Ponemon Institute study, 59% of companies have experienced a third-party data breach. And in light of this fact, 51% of CISOs now see the failure to control third-party data usage as a major security concern.

When collaborating with third-party service providers, you need a way to maintain control of your sensitive data. With proper endpoint security, you can control access and mitigate the risks of a third-party breach.

Learning from major third-party data breaches

In 2013, in one of the largest data breaches of all time, retail giant Target’s systems were compromised as a result of credentials stolen from a third-party HVAC vendor.

The stolen credentials gave attackers access to the HVAC vendor’s billing, contract submission, and project management platform. This level of access helped attackers compromise the HVAC vendor’s VPN credentials for Target’s network. And, as a result, the attackers were able to remotely access Target’s ...


Read More on Datafloq

Five Small But Important Things to Observe in AI and Machine Learning

Regardless of how fascinating future of AI might be, there would always be some amount of scepticism coupled with the subject. Movies have conditioned us to believe the age of Robots might take over humans but people often ignore the upside of AI and Machine learning. If utilised well, mankind could do wonders with smart machines. They could impact our daily lives immensely and most certainly, life would get easier. For businesses, it could change the way people interact with their products and services. It could be as redefining as changing the logo of a company. Some people believe that a minor change of logo could do a lot more to a company’s reputation than one could imagine. You might want to do that as well with the help of a brand like Trailor brands that has been helping brands for ages.

Coming to the point, beyond a shadow of a doubt, the subject of AI and Machine Learning has always been a hot topic of discussion in recent years. It has and will impact our lives in unimaginable ways and we’re here to discuss some of those small things that might be more significant than we realise. Let’s have a ...


Read More on Datafloq

Ola set to go on a self-drive trip with $500M in boot

The investment will come from a mix of debt and equity over the next couple of years, according to sources.

AI tools help tech firms find the right hand for the job

Companies are tapping into a mix of technologies like AI, natural language processing and machine learning to drive value from the large stores of data they have accumulated over the years.

Wednesday, 27 March 2019

In 2019 - Being Data-Driven Enterprise Is A Blessing Or A Curse!

Data is everywhere; it can give both an accurate insight and the fog of visibility at the same time. Over recent years, streaming data technologies has become the latest norm whether it’s through the internet of things (IoT), Mobile and Big Data Pipelines. Several businesses are found struggling to put better data management practices.



Unrelenting pressure from non-traditional players is seen driving organizations transform themselves digitally. Generating massive amounts of data from new sources is no longer a luxury; it has become a necessity Right from customer information to financial transaction, product and service purchase histories, customer journeys, marketing campaigns, service inquiries, market feeds, social media streams, Internet of Things (IoT) streams, software logs, and text messages (including emails and SMS), plus those newer sources, companies require capturing it all.

Although the data management landscape seems to have evolved in 2018, in 2019 it will be even more transformative. Let me show you why!

#1 Data protection and Data transparency

With increasing strong demands from end consumers, enterprises are seen accommodating improved data protection across platforms, social media channels, cloud applications and more. Day in day out, demand for transparency seem to have increased regarding how data is collected, aggregated and shared. To ...


Read More on Datafloq

How the Internet of Things is Revolutionizing Urban Living

Technology doesn't serve any purpose or function if it cannot increase the quality of life for the average person. After all, this should be the ultimate goal used for guiding society's movement into the future. With our current wealth of technological resources and understanding, it should be a fairly easy transition to improving the lives of everyone in society. Fortunately, there are many cities already leveraging IOT to improve living standards. Here are just a few ways in which IOT is revolutionizing urban living.

Reducing Congestion on the Highways

No major city is free from the plague of traffic congestion. With millions of people hitting the roads at the same intervals, it is nearly impossible to have any organization. However, many cities are leveraging IOT to help reduce congestion on the roads and highways. Some of the primary initiatives include directed parking, safer roads, smarter traffic systems, frictionless toll and more. According to Juniper Research, these technological additions can help drivers get back 60 hours each year that would have been wasted stuck in traffic or waiting for a parking spot.

Improving Air Quality

Every large city suffers from poor air quality. This is a direct result of the traffic issues that were discussed ...


Read More on Datafloq

US pushes Chinese owner of Grindr to divest the dating app: Sources

Grindr deal highlights the pitfalls facing Chinese acquirers of US companies seeking to bypass the CFIUS review system, which is based mostly on voluntary deal submissions.

Harnessing Machine Learning And Artificial Intelligence To Accelerate Discoveries

By Melissa Pandika

As increases in computational power allow researchers to churn out massive amounts of data, scaling up analysis of it has grown ever-more challenging. At the Molecular Medicine Tri-Conference in San Francisco, a multidisciplinary cadre of thought leaders tackled this issue in the Machine Learning and Artificial Intelligence track. They shared their latest applications of these technologies to large-scale data to streamline drug discovery, clinical trials, and diagnostics.

The Machine Learning and AI track launched on Monday with a focus on translating data into therapies. Asim Siddiqui, chief technology officer at NuMedii, discussed how his company develops technologies that allow for the integration biological data and creation of models that can predict drug-disease pairings. By accurately predicting these pairings, NuMedii aims to improve the probability of successful clinical trials. Siddiqui pointed to the high failure rates of clinical trials and noted that even modest decreases in these failure rates provide value. He described NuMedii’s Artificial Intelligence for Drug Discovery (AIDD) technology, which integrates data from the literature, as well as -omics and other data from partner and public data streams, to the common platform, where his team can run analytical algorithms for discovery. AIDD spans hundreds of diseases, plus thousands of compounds and targets.

NuMedii’s technology has led to a number of promising predictions. For instance, imipramine, a tricyclic anti-depressant, was predicted to have anti-cancer activity associated with tumor cell apoptosis. Indeed, the compound has shown activity against multiple small cell lung cancer models in vitro and in vivo. Siddiqui concluded by noting that “you don’t need a huge team to accomplish a lot,” pointing to the wealth of data in the public domain that his team has mined with machine learning.

The next series of talks looked at efforts to develop AI automated diagnostics. Ryan Amelon from IDx Technologies shared findings from a clinical trial of IDx-DR, an AI system designed to detect diabetic retinopathy in adults, which led to FDA approval last April as the first-ever fully autonomous diagnostic for use without a specialist. Ophthalmologists “are not so great” at classifying diabetic retinopathy, a leading cause of blindness in the working age population, with sensitivity ranging from 33 to 73%, Amelon said. The clinical trial of IDx-DR, which included 900 patients across 10 sites, found it to be 87.2% sensitive and 90.7% specific, although Amelon said he and his team remain blinded to the data. , Highly experienced, certified retinal photographers obtained the Early Treatment Diabetic Retinopathy Study (ETDRS) reference standard—the “gold standard” for measuring diabetic retinopathy severity–which was then compared to IDx-DR’s output.

Amelon then outlined how IDx-DR meets the qualifications of a fully autonomous AI system. He highlighted its usability, noting that operators need only a high school diploma and must have no experience using a fundus camera. What’s more, the system determined that 96% of exams in the FDA study were of diagnostic quality. IDx-DR also directs operators to retake poor-quality images. Finally, the output is actionable, and the system rigorously validated. In fact, Amelon and his team have developed algorithms that separately detect various biomarkers of diabetic retinopathy, resulting in roughly 12 points of validation instead of just one. When it comes to developing a fully automated diagnostic to replace the physician, “there’s a lot more that goes into it besides just training the algorithm,” Amelon said.

Joel Saltz, chair and professor of biomedical informatics at Stony Brook University, discussed a multi-institutional collaboration to develop a deep learning-based computational stain for tumor-infiltrating lymphocytes, described in Cell Reports last April (DOI:10.1016/j.celrep.2018.03.086). Patterns of tumor-infiltrating lymphocytes were associated with cancer type, clinical outcome, and tumor and immune molecular features. For instance, those that border the tumor—which the tumor prevents from penetrating—were associated with poorer outcomes. “The goal here is not to replace pathologists,” but to find biomarkers to determine what kind of therapy would make sense, Saltz said.

Aalpen A. Patel from Geisinger then described the company’s development of a deep learning algorithm trained using a large, diverse set of medical imaging data to identify intracranial hemorrhage on head CT scans and to help physicians prioritize patients for diagnostic screening. Intracranial hemorrhage accounts for around two million strokes every year, and almost half of deaths occur in the first 24 hours of an intracranial hemorrhage. In the clinic, the algorithm lowered the time to diagnosis of new outpatient cases of intracranial hemorrhage by 96%.

Patel emphasized that using a large volume of heterogenous, clinical-grade data is essential to developing clinical tools. He also noted that, amid the emergence of AI-based clinical tools, what physicians do will change. AI has and will improve patient care, and he encouraged physicians to be more involved in its evolution. And while it’s important to ask how AI can help physicians care for patients, “the question we should really be asking is, ‘How can I help the patient?’” he said. “That means you can improve operations and post-visit care, as well.”

On Tuesday, Imran Haque, former chief scientific officer of Freenome, gave the first of a series of talks on machine learning for biomarker discovery, which delved into the two major approaches to biomarker discovery, as well as their limitations and how to address them. Recognizing the high cost of sample acquisition, the mechanistic approach uses the fewest samples possible at each step, identifying a biological mechanism and progressing to retrospective and prospective follow-ups—but with no guarantee that performance will generalize. The more machine learning-driven empirical approach, on the other hand, acknowledges that biology has too many “unknown and unknowns” to form useful hypotheses from the outset and instead lets the data speak for itself, relying on collecting lots of data and creating statistical models–but statistical methods require more data than they appear to initially. Again, the cost of attaining samples remains a core problem.

Haque cited examples of how both approaches have failed and suggested bridging the gap between them as a solution and address the issue of small data set sizes in biology. He noted that statistical methods will always take the easiest way out to get the answer you “want.” (For instance, the lack of a test set will lead to overfitting to the training set.) As a result, you’ll be right for the wrong reasons, which “still looks right to the algorithm,” he said. Instead of designing a discovery project to work, he suggested designing it to not fail—that is, to design around all the ways the algorithm could “cheat.” The biological mechanism could allow you to define positive and negative controls, providing invariants you could enforce on or teach to a model, potentially helping to solve the empirical sampling problem.

Later in the session, Marina Sirota, an assistant professor at the Bakar Computational Health Sciences Institute at UCSF, described her lab’s use of computational integrative methods to identify the determinants of preterm birth. She detailed how she and her team have integrated pollution exposure datasets with birth record datasets, both within California, enabling them to pinpoint two water contaminants associated with preterm birth.  Meanwhile, their meta-analysis of three studies using maternal blood samples identified a maternal transcriptomic signature of preterm birth, which showed an upregulation of innate immunity and downregulation of adaptive immunity. They saw the reverse in the fetal transcriptomic signature of preterm birth and are further investigating this inverse relationship between maternal and fetal transcriptomic signatures.

On Wednesday, Kevin Hua of Bayer Digital Health looked at AI in drug discovery, beyond conventional data sciences. Hua outlined tasks that clustering, classification and other conventional machine learning technologies can accomplish—such as image classification and protocol design—followed by tasks that require advanced AI technology—such as biological modeling, and time and cost reduction. The problem is, many tasks in drug discovery can’t be accomplished by machine learning technologies and need advanced AI technologies, he said. He emphasized model-based reasoning, which can model how molecules work in biological systems and could be used for structured mapping of small molecules, for instance.

A session on developing tools in support of clinical data mining for diagnostic innovation closed out the Machine Learning and AI track. Renee Deehan Kenney of PatientsLikeMe described the development of the DigitalMe explorer and researcher platform that helps researchers glean meaningful health insights from complex biological datasets via machine learning and other modeling techniques. Guergana Savova, an associate professor at Harvard Medical School, then discussed DeepPhe, software that uses natural language processing to extract deep phenotype information from cancer patients’ electronic medical records, including temporality—meaning it places surgeries and other events on a timeline, crucial for measuring disease activity, for instance.

Harry Glorikian, a healthcare consultant and author of MoneyBall Medicine: Thriving in the New Data‐Driven Healthcare Market, moderated a panel discussion featuring Savova and Kenney that focused largely on the first place where they see their work making the biggest impact. Savova foresees DeepPhe having some kind of human-assisted application until the error rate improves, although she noted that what would qualify as an acceptable error rate in clinical care remains unclear. She likened DeepPhe’s first application to cruise control in cars “so that the patient can always intervene… before we go to complete autonomous cars,” she said.

Since PatientsLikeMe is a website where patients can connect with other patients and track information about their health, further down the road, Kenney envisions “being able to show every person a window into what’s going on in their body and allow them to track this over time. If you decide to go on the paleo diet, will that matter?” In other words, citizens could use the platform to essentially conduct longitudinal experiments on themselves. Eventually, machine learning and AI may emerge as research tools that empower not only scientists and clinicians, but patients, as well.

Data Engineering Light: Expanding Choice for Production Workloads

Adding a Lower Tier, Lower Price Option

Since its inception a guiding principle for Databricks has been the unification of data science and engineering helping to bring together silos and raising the level of collaboration and productivity among the analytics professionals. And our customers have been reaping the benefits of this to solve some of their most challenging problems. While our customers see a lot of value in our Unified Analytics Platform, we recognized the need for a lower-priced option suitable for running simple production jobs at scale.

We are proud today to announce the general availability, of Databricks Data Engineering Light (formerly known as Databricks Basic, priced currently at $0.07/DBU), a new addition within our Unified Analytics Platform, aimed at running production jobs on Databricks’ equivalent of open source Apache Spark™.  Using Data Engineering Light is easy – you simply select the “Light” cluster runtime version when scheduling your job.

Data Engineering Light provides a runtime option for jobs that don’t need the advanced performance, reliability, or autoscaling benefits provided by the more capable Databricks Data Engineering offering. You can select Databricks Light only when you create a cluster to run a JAR job, spark-submit job, or Python job (via API); you cannot select this option for clusters on which you run interactive or notebook job workloads.

Beyond the capabilities offered in Databricks Data Engineering Light, the Unified Analytics Platform offers other more advanced offerings with capabilities such as:

  • Databricks Delta – for adding data reliability and performance to data lakes
  • Runtime for ML – for working with ML frameworks of your choice
  • Workspace – enabling highly effective collaboration among data professionals
  • Interactive clusters – supporting high concurrency use for multiple users
  • Ecosystem integrations – for being able to work with a range of third-party BI tools

Customers now have increased choice and flexibility and improved cost-effectiveness when it comes to running their analytics production workloads on Databricks’ platform.

If you are not already using Databricks, I invite you to try Databricks today.

--

Try Databricks for free. Get started today.

The post Data Engineering Light: Expanding Choice for Production Workloads appeared first on Databricks.

Introducing Databricks AWS IAM Credential Passthrough

As more and more analytics move to the cloud, customers are faced with the challenge of how to control which users have access to what data. Cloud providers like AWS provide a rich set of features for Identity and Access Management (IAM) such as IAM users, roles, and policies. These features allow customers to securely manage access to their sensitive data.

Challenges with Identity and Access Management for analytics in the cloud

We see many AWS customers attempting to solve this challenge either using a combination of IAM roles and IAM Instance Profiles or with hard-coded IAM credentials. Customers use policies for controlling access to data on S3 or other AWS resources such as Kinesis, SQS, etc. They then use Instance Profiles to pass IAM role information to EC2 instances. This allows them to associate a single data access policy with EC2 instances upon bootup.

There are some challenges and limitations with these options, however:

  1. EC2 instances can only be assigned a single Instance Profile. This  requires customers to run different EC2 instances each with different roles and access controls to access multiple data sets, thereby increasing complexity and costs
  2. Whether using Instance Profiles or hard-coded IAM credentials, users on an EC2 instance share the same identity when accessing resources, so user-level access controls aren’t possible and there is no audit trail of which user from an EC2 instance accessed what, with cloud-native logging such as Cloud Trail.
  3. Neither Instance Profiles or IAM credentials necessarily map to the access policies in the enterprise identity management system, such as LDAP or Active Directory groups, which requires managing a new set of entitlements across different systems

To work around these limitations, we see some customers turning to legacy or non-cloud native tools that broker access to cloud storage such as S3. Many of these tools were not designed with cloud storage in mind and in some cases might require custom extensions to Apache Spark with support for limited formats or poor performance. This places an additional operational burden on the customer in order to manage a new set of services and entitlements and negates the scalability, low-cost, and low-maintenance of directly using S3. For Databricks customers, these tools might also impact the usability and features we provide in the Databricks Runtime such as DBIO and Delta.

When we began designing a solution, our top priority was to integrate with the identity and access services provided natively by AWS. In particular, our focus was to leverage AWS Identity Federation with SAML Single Sign-On (SSO). Given that Databricks already supports SAML SSO, this was the most seamless option for having customers centralize data access within their Identity Provider (IdP) and have those entitlements passed directly to the code run on Databricks clusters.

  1. First, the customer configures a trust relationship between their IdP and AWS accounts in order for the IdP to control which roles users can assume
  2. Users login to Databricks via SAML SSO, the entitlement to the roles are passed by the IdP
  3. Databricks calls the AWS Security Token Service (STS) and assumes the roles for the user by passing the SAML response and getting temporary tokens
  4. When a user accesses S3 from a Databricks cluster, Databricks runtime uses the temporary tokens for the user to perform the access automatically and securely

IAM credential passthrough with Databricks

In order to use IAM Credential Passthrough, customers first enable the required integration between their IdP and AWS accounts and must configure SAML SSO for Databricks. The rest is managed directly by the IdP and Databricks, such as what roles a user has permission to use or fetching temporary tokens from AWS.

In order to use federated roles, a new cluster configuration is available called “Credential Passthrough”.

This requires a High Concurrency cluster and currently supports Python and SQL only, but R support is in development. With those languages, we can run user code in an isolated, low-privileged process outside of the Java Virtual Machine, where Spark executes, but we cannot with Scala. Since different users on a cluster all have different identities when accessing S3, it’s critical to isolate user-code and prevent leaking credentials across different sessions.

A new API specifically for federated roles is available to users as part of DBUtils. First, users can list the roles they have available by calling dbutils.credentials.showRoles

In order to assume a specific role, user calls dbutils.credentials.assumeRole

From that point on, any S3 access within that notebook will use the chosen role. If a user does not explicitly assume a role, then the cluster will use the first role in the list.

In some cases, users may need to analyze multiple datasets that require different roles. In order to make the role selection transparent, bucket mounts can be used as well:

dbutils.fs.mount(

 “s3a://databricks-demo-data-us-east-1/data/sensitive”,

 “/mnt/sensitive-data”,

 extra_configs = {

“fs.s3a.credentialsType”: “Custom”,

“fs.s3a.credentialsType.customClass”: “com.databricks.backend.daemon.driver.aws.AwsCredentialContextTokenProvider”,

“fs.s3a.stsAssumeRole.arn”: “arn:aws:iam::997123456789:role/sensitive-data-role”

})

dbutils.fs.mount(

“s3a://databricks-demo-data-us-east-1/data/hr”,

“/mnt/hr-data”,

extra_configs = {

“fs.s3a.credentialsType”: “Custom”,

“fs.s3a.credentialsType.customClass”: “com.databricks.backend.daemon.driver.aws.AwsCredentialContextTokenProvider”,

“fs.s3a.stsAssumeRole.arn”: “arn:aws:iam::997123456789:role/hr-data-role”

})

With bucket mounts defined, users can reference the DBFS paths without needing to select any roles. Users who can’t assume the role specified by the mount point will not be able to access the mount point. This also allows joining across datasets requiring different roles and makes the experience seamless for end-users.

Since federated roles uniquely identify the user, it allows for more powerful data access controls on S3. One example would be to define “home directories” on S3 that let users save datasets to private locations only accessible by the owner. This is possible even though users may be sharing the same IAM role or even the same cluster.

Since assumed roles identify the individual user, auditing is now available by simply enabling S3 object logging via Cloud Trail. All S3 access will be tied directly to the user via the ARN in Cloud Trail logs.

Conclusion

With the new IAM Credential Passthrough feature, we’re introducing more powerful data access controls that integrate directly with your enterprise identity platform. It provides seamless access control over your data without losing the reliability, scalability, and low-cost of S3. With Databricks process isolation you can still have the efficiency of safely sharing Spark cluster resources without having to manage additional tools in your environment. You can safely let your analysts, data scientists, and data engineers use the powerful features of the Databricks Unified Analytics Platform while keeping your data secure! IAM Credential Passthrough is in private preview right now, if you’d like to know more please contact your Databricks representative.

 

--

Try Databricks for free. Get started today.

The post Introducing Databricks AWS IAM Credential Passthrough appeared first on Databricks.

Tuesday, 26 March 2019

Role of AI & ML in Network Monitoring

Network monitoring deals with detecting the slow components present in the network like overloaded or frozen servers, failed switches, failing routers, or any other problematic devices. By and large, network monitoring is carried out with the help of Network monitoring software applications and tools. It is used to detect whether a given web server is functioning and connected correctly to the networks present across the world. It provides complete visualization of both the network and the internet.

The network managers and monitoring team usually take the help of software to detect any issue in the network but still, it becomes tough for them to identify some of the problems and in such situations, there is need of more advanced techniques. The managers would be happy to incorporate some of the latest technologies and strategies to monitor the networks in their organizations.

This is a modernized era, and latest technologies are making a lot of digital transformations in various fields out there. Network management software is one of them. The new advancements with machine learning (ML) and artificial intelligence (AI) technologies have transformed the lives of professionals, and the lifestyle of people that has brought many drastic changes in marketplaces. Similarly, they are ...


Read More on Datafloq

Importance of Data In Assembly Lines Modernization

Henry Ford was not the first to use an assembly line process for manufacturing, he was one of the most innovative. He reached higher efficiency on his production floor than his competitors and did it all without the analytics we have today. While not all of his techniques are proving to be as useful in office settings as they are in the factory, other industries can learn a lot from Ford's methods.

One of the most useful things to take in is to focus on doing things in a way that helps achieve peak productivity rather than the way it has always been done.

The reason we still work an eight-hour workday, five days a week is because that was the optimal production schedule Ford discovered 100 years ago. Today, however, many businesses are taking a page out of Ford's book by exploring a shorter workweek and flexible schedules.

What worked best in a factory almost a century ago may not help the modern workforce achieve the greatest results. Whether in the office or on the factory floor, the key to optimizing efficiency is data. Without data, we wouldn’t know for sure what are the bottlenecks, where the productivity decreases, and what are ...


Read More on Datafloq

Data Scientist Guides AI-Driven Customer Engagement at Uber

Micro-Movements in Driving Behaviors: Crucial for AI Self-Driving Cars

By Lance Eliot, the AI Trends Insider

I was eagerly awaiting making a right turn on a red light at a busy intersection that led onto the always hectic Pacific Coast Highway (PCH). Dutifully stopping behind the crosswalk line, I could not quite see the prevailing traffic that was coming over a hilly portion of PCH that fed down through this particular area. The layout meant that you could not be fully sure whether the lanes were free to drive into and at any moment a raging car might seemingly leap over the hill and barrel into you if you prematurely had opted to make the treacherous right turn.

Being in a hurry, I decided that I would just go for it and gunned my engine to swiftly propel my car through the right turn and into the lane of traffic that seemed to be empty and available. Unfortunately, in the split second that I had hit the gas, sure enough a car appeared suddenly at the crest of the hill and I could see that it was intent on using the lane that I was about to occupy. Some drivers in my shoes might proceed anyway and force the other driver to either hit their own brakes or swerve to another lane to avoid me, the lane intruder.

I didn’t want to chance the possibility of having the nearing car rearend my car, plus I would obviously have been in the wrong for my actions. I urgently moved my foot from the accelerator over to the brake and jammed down hard, forcing my car to come to a heart stopping halt. Yes, I was protruding somewhat into the lane, but it was not enough to cause the other car to undertake a diversion or hit their own brakes. They could sweep past my now half-in half-out car and proceed without skipping a beat.

The story might end there, and I suppose might have been unworthy perhaps of telling if that’s all that happened, except for one little and quite important fact. There was a car that was behind me, also waiting at the red light, and the driver had been apparently hopeful of making the right turn as I did so. This car had inched its way up to my rear bumper and was earnestly urging me to make the right turn swiftly. I’m sure you’ve had this kind of eager beaver behind you. They breath down your neck and get irritated and upset if you don’t make a highly expedient right turn on red.

Well, this other driver had assumed that since I was gunning my engine and leaping forward, they might as well do the same. I guess we were going to be like two sprinters that heard the starting gun go off and it was then a race to get underway. When I opted to hit my brakes, doing so suddenly, the driver behind me was caught by surprise.

Allow me a moment to carp about that driver. In my view, if they are going to follow behind me with nary any space allotted between the two of us, it is incumbent upon that driver to be fully ready to react in whatever way the driver ahead of them acts. It is a solemn golden rule of follow-the-leader in car driving. Anyone that is going to play the follow-the-leader game and do so without a safety cushion, they need to mirror the actions of the leader and do so with the same quickness of any efforts by the leader. Anyone that can’t do that should not be participating in follow-the-leader. Just wanted to get that off my chest.

In any case, the car behind me almost rammed into my car. Thankfully, it was almost and not an actual hit. The driver swerved to avoid hitting the back of my car and jammed against the curb where the right turn was. I could see in my rearview mirror that the driver was upset – at me, and was apparently cursing and making unmentionable finger movements expressing their displeasure at my actions.

I usually try to calculate the moves of any cars behind me in whatever driving actions I take, and likely normally would have realized the other driver was within an inch of my car and not therefore have rocketed forward, but in this case I was in a hurry and assumed the other driver would be savvy enough to realize the potential for a rapid braking due to the PCH hilly road shape. Too bad for them that they miscalculated. It didn’t look like there was any actual damage to their car and so I proceed to now continue the right turn and make my way to my looming appointment.

I did have in the back of mind a concern that this driver might get themselves worked up into a road rage. You know how that can happen in these kinds of circumstances. The driver was mad at me. They were in a hurry. I fully expected the driver to now also make the right turn, and then attempt to catch-up with me, coming alongside of my car to make more reprehensible gestures at me. Or, perhaps even try to run me off the road. You never know what crazy things people might do.

For my article about road rage, see: https://www.aitrends.com/selfdrivingcars/road-rage-and-ai-self-driving-cars/

For the foibles of human drivers, see my article: https://www.aitrends.com/selfdrivingcars/ten-human-driving-foibles-self-driving-car-deep-learning-counter-tactics/

When you think about the situation, I probably accelerated for just a fraction of a second or so. My car appeared to leap forward like a leopard. The total distance covered was just a handful of feet. Luckily my brakes were darned good and when I jammed my foot onto the brake pedal the car came to an amazingly crisp stop. You could almost say that the whole thing happened in the blink of an eye.

I’m not sure that any of the other nearby drivers noticed what happened. The car that was barreling down PCH that I had wanted to avoid hitting was probably just happy that my car was not in their way. Other cars at the intersection that were waiting to go were probably looking at the red light and expecting it to soon turn green. All in all, I’d bet that only me and the car behind me were aware of the dance that the two of us undertook.

I’ve told you this story to bring up something that is quite important and yet often overlooked as an aspect of human driving behavior, namely the use of micro-movements when driving.

A micro-movement is considered a somewhat subtle driving action that exists somewhere in the gray area between doing an overt and obvious driving maneuver and doing essentially no noticeable driving maneuvering at all.

Some Drivers Oblivious to Micro-Movements

Some human drivers are oblivious to the nature of micro-movements. These drivers are blind to noticing micro-movements and can’t readily tell you what a micro-movement looks like. Their attention to the driving task is based on wholescale movements and maneuvers, ones that are stand out like a brass band and are so obvious that you’d truly need to be blind to miss them. Anything less than the outright maneuver of a car is totally lost on them.

I remember one colleague at work that used to drive several of us out to lunch on Fridays. He drove his car like it was a Sherman tank. He was master of the roadway, and no one dared to get in his way. This vividly became evident on one occasion involving him and a car that appeared to be driven by someone lost and unfamiliar with the local streets.

The driver ahead of us was driving relatively slowly in terms of being just a tad below the posted speed limit. The car had several times started toward the curb as though the driver was thinking of stopping or parking, and yet the driver continued ahead. It seemed apparent to me and everyone else in the car that my colleague was driving that this other driver was trying to figure out where they were and wanted to maybe park at a specific address.

My clueless colleague did not notice the antics of the other car. Driving his car like a take-no-prisoners tank commander, he nearly rolled over the other car when it tried to make a few of those potential darts to the curb. If it had been me doing our lunch journey driving, I would have given the other car some added space to allow them to do these swaying kinds of motions. Plus, I would have swung into the left lane and gone around the other car, hoping to then avoid the situation entirely of dealing with the other car.

After we made it to the lunch, I asked my colleague if he had noticed the car that had been in front of us. What do you mean, he asked, what was to be noticed? When I told him about the micro-movements of the other driver, my colleague insisted that I was making it up. It was a gag, he insisted. Fortunately, my fellow lunch time eaters all chimed in about that car. They had all noticed it too. Of course, this only inspired my colleague to claim that it was a conspiracy and we were all ganging up on him.

For my article about conspiracies, see: https://www.aitrends.com/selfdrivingcars/conspiracy-theories-about-ai-self-driving-cars/

For the need to use defensive driving techniques, see: https://www.aitrends.com/selfdrivingcars/art-defensive-driving-key-self-driving-car-success/

You don’t necessarily need to be in a car to see the micro-movements of other cars. I ride my bike quite a bit and anyone that is hopeful of surviving the riding of a bike in city traffic is likely to be an expert on the micro-movement evidenced by cars and those wacky human drivers.

Just last week, I was in the bike lane, minding my own business, and I had a car that zoomed along and passed me, doing so with a flourish. The car traffic up ahead was stopping to deal with a roadway construction effort, and I lamented in my mind that this “jerk” was going too fast toward it. In my book, the driver ought to be slowing down and getting ready to figure out how to safety navigate around the street repair efforts.

Due to the bunching up of the car traffic, I caught up with the traffic since the bike lane was unimpeded. It was one of those rare cases whereby you can go faster along than the cars can. The cars were jockeying for position to find a means to navigate around the street crew and meanwhile I was rocketing ahead on my bike. Though I relish a moment of going faster than cars, I also know that doing so is tantamount to poking at a slumbering beast that can awaken at any moment and attack you.

Remember the car that had zoomed past me? It became the slumbering beast. The driver was anxious about being snagged in the snarled traffic. I could see that the car was nudging somewhat into the bike lane. At first, it was merely a slight intrusion. I had a hunch that the driver was testing the waters about whether or not he could come directly into the bike lane, doing so to use it as a means to escape the stunted traffic and get ahead of the other “sheep” that were stuck in the pile of cars.

I moved to the rightmost position of the bike lane and was kissing the curb. This was a defensive preparatory move by me. I also had my hands squarely on the brake handles of my bike, ready to use my brakes if needed. I observed the tires of the car that was inching into the bike lane and could see that the driver was becoming more decisive about angling toward the bike lane.

Sure enough, he then maneuvered completely into the bike lane, which was not only dangerous for me, but there were other bike riders in it too. The madcap driver completely upended all of the bike riders, not by the car actually hitting the riders, and instead by the bike riders all swerving this way or that way to avoid the intruding car, like bowling pins at a bowling alley. Have you ever seen those televised bike races in France and seen those moments when the bike riders appear to fall and collide all at once, it was about the same here. Sacre Bleu!

Sometimes a micro-movement can be signaled by something other than the car itself. For example, a human driver puts their arm out the driver’s side window to wave you around them, or maybe they point at something up ahead to forewarn you that there’s a big pothole in the road. Hand motions though can be confusing and at times not meant to be a car movement indication.

I watched the other day as one driver put his hand and arm fully out the window and seemed to be waving the appendage up and down, rather frantically, which I wasn’t sure what this might portend.  Maybe the earth had opened a giant sinkhole and the road ahead was merely a huge ditch? When I opted to drive around the car, figuring I’d need to be on my toes and contend with whatever I might encounter, I could hear loud music being played on the speakers in the car — I surmised that the hand and arm motions were just his way of keeping a beat to the music.

A driver’s head can be another form of a clue about driving behavior. I’m sure you had times when you’ve been driving and could see the head of the driver in the car ahead of you. The driver perhaps turns their head to the right, and you are able to predict that the driver is going to try and either swing into the lane to the right or start to make a right turn. Another head signal involves a head nod, often used when at a four-way stop and the other driver wants to showcase that either you should go or they want to proceed.

For my article about the head nod problem in AI self-driving cars, see: https://www.aitrends.com/selfdrivingcars/head-nod-problem-ai-self-driving-cars/

For why humans are going to be pranking AI self-driving cars, see my article: https://www.aitrends.com/selfdrivingcars/pranking-of-ai-self-driving-cars/

For the dangerous practice of people jumping out of moving cars, see: https://www.aitrends.com/selfdrivingcars/shiggy-challenge-and-dangers-of-an-in-motion-ai-self-driving-car/

For aspects of how we use conspicuity when driving, see my article: https://www.aitrends.com/selfdrivingcars/conspicuity-self-driving-cars-overlooked-crucial-capability/

Let’s focus on the micro-movements of cars that are evidenced by the actual car and set aside for now the kinds of micro-movements that humans themselves might undertake with their own bodies.

Here are some the key ways that micro-movements can appear:

  •         Angling of the wheels of the car
  •         Leftward leaning of the car
  •         Rightward leaning of the car
  •         Stuttered motion of the car (start/stop)
  •         Car lurches (sudden motion)
  •         Riding of the brakes
  •         Etc.

These aforementioned car movement indications are considered micro-movements when they are done in a subtle manner. It’s a kind of a tease. Just a little bit of showing of the leg, as it were.

Some might liken this to a “tell” in a sport like basketball. When playing basketball competitively, you might give a movement of your head that seems to say you are going to make a run to the basket, and the other player covering you can potentially read the tell and get positioned to block you. This can all happen in an instant. It can happen too before the player with the ball has actually moved their feet toward the basket and taken any other overt actions.

Your Tell Gives You Away

If you like to play poker, you certainly know about the tell or clues that other players often inadvertently show. When I was in college, I played a Saturday poker game with some of my buddies. One member of the tight knit group was well-known for trying to bluff his way on hands where he had nothing of use in his cards. It was the classic saying about the notion that weak means strong, and strong means weak, namely that whenever he tried to make it seem like he had all aces, it really meant he had junk. This was his tell.

Another player had a tell that involved pushing his glasses up his nose and toward his eyes. We all eventually figured out that this was his tell. It meant that he was lying about whatever he might be saying. None of us revealed the tell to him, and we kept it each to ourselves, relishing it. After months of this, one of the pack opted to tell him about his tell.

At first, he was angry that we had noticed the tell. He was also angry that we had not revealed it to him, though he sheepishly realized that he probably would not have told any of us about our tells. It dawned on him that if we had become conditioned to his tell, he could use that conditioning to his advantage. For a few weeks, he would purposely push his glasses on his nose, doing so to make us think he was lying, even though he might be telling the truth. We got caught like a hungry fish on a shiny lure. He made some good money by exploiting his own tell.

One of the members of the group had been oblivious to the tell. When it was finally revealed to everyone at one of the poker games, this particular member was shocked. What tell? He had never noticed that our friend was often pushing his glasses up further on his nose.

This highlights my earlier point about micro-movements of cars. Some people notice them, some do not. And it also highlights another very important point. You cannot for sure bet on a micro-movement since there is no guarantee it will ultimately graduate into becoming a full-on movement. Not all tells blossom. You need to assess each one, along with the situation at-hand, and ascertain whether the micro-movement is telling you something of substance or not.

Let’s also make clear what isn’t a micro-movement. If someone completely cranks the wheels over to make a full turn, I don’t think we can really consider that to be a micro-movement. Only if the driver had just slightly angled the wheels, perhaps doing so to get ready for a turn, or maybe they were almost subconsciously starting the turn, those are situations of a micro-movement.

One of the ways to study micro-movements often involves observing teenage novice drivers. There are some newbie drivers that stretch out every driving move they make. When they are told to make a turn at the corner up ahead, they start the turn a quarter mile beforehand. When they see a stop sign, even if its at a distance of a football field length away, the novice driver often starts to tap those brakes, getting the car slowed down, slowly, really slowly.

Those tend to be exaggerated micro-movements. Most seasoned drivers are apt to make much more subtle micro-movements. Those weathered drivers have become ingrained in how they drive a car. They aren’t necessarily aware that they are using micro-movements.

Similar to my story about my poker playing friend that ended-up exploiting his tell, there are some savvy drivers that like to use their micro-movements in an exploitive way. These drivers often assume that other drivers will notice the micro-movement and then back-off or otherwise give way to the driver that is using the micro-movements.

This happens a lot in parking lots. When cars are driving around and around trying to find a parking spot, it can become a gamesmanship effort of who suggests they found an open spot first. You’ve likely seen two cars that came open an open spot, and the cars are facing each other head-to-head. Which one will get the spot? If one driver angles their wheels, it can be a kind of assertion that they have tagged the spot and are going to aggressively make their way into the spot.

This can at times create more problems than it solves.

Suppose you have two cars facing each other, trying to vie for a parking spot, and each makes a wheel angling micro-movement. Is this a tie game? Will one driver take Umbridge that the other driver appears to be claiming the spot?

We also need to consider the clueless driver that doesn’t even notice the other car is angling its wheels.

Or, you can also have the driver that sees the micro-movement and opts to ignore it, acting as though they didn’t notice it, so they can appear to be blissfully ignorant as they take the spot (and later claim they didn’t realize the other car was trying to go for it). That’s a sly move.

Besides teenage novice drivers showcasing micro-movements, there’s another kind of driver that can do likewise, namely a drunk or DUI (Driving Under the Influence) driver. When someone is just partially drunk, they often exhibit numerous micro-movements. They do so because they are either unsure of what driving path they are going to take, or they are lacking in their mental facilities and are erratically coping with the car controls.

If you are a seasoned driver that watches for drunk drivers, I’m sure you’ve seen many such micro-movements. That being said, once a drunk or DUI driver has gotten fully engulfed by their mind-altering influence, their car directing movements are bound to become the opposite of micro-movements. The driver make overt and extremely overstated movements. Any subtlety about their driving is now completely gone.

For more about drunk driving, see my article: https://www.aitrends.com/selfdrivingcars/dui-drunk-driving-self-driving-cars-prevention-cure/

For tit-for-tat driving behaviors, see my article: https://www.aitrends.com/selfdrivingcars/tit-for-tat-and-ai-self-driving-cars/

For my article about rocket man styles of driving, see: https://www.aitrends.com/selfdrivingcars/rocket-man-human-drivers-ai-self-driving-cars-outrunning/

For the role of greed in driver behavior, see: https://www.aitrends.com/selfdrivingcars/selfishness-self-driving-cars-ai-greed-good/

What does this have to do with AI self-driving cars?

At the Cybernetic AI Self-Driving Car Institute, we are developing AI software for self-driving cars. One aspect that is not yet getting much attention involves the micro-movements of driver behavior, which some consider an edge or corner case problem.

Allow me to elaborate.

I’d like to first clarify and introduce the notion that there are varying levels of AI self-driving cars. The topmost level is considered Level 5. A Level 5 self-driving car is one that is being driven by the AI and there is no human driver involved. For the design of Level 5 self-driving cars, the auto makers are even removing the gas pedal, brake pedal, and steering wheel, since those are contraptions used by human drivers. The Level 5 self-driving car is not being driven by a human and nor is there an expectation that a human driver will be present in the self-driving car. It’s all on the shoulders of the AI to drive the car.

For self-driving cars less than a Level 5, there must be a human driver present in the car. The human driver is currently considered the responsible party for the acts of the car. The AI and the human driver are co-sharing the driving task. In spite of this co-sharing, the human is supposed to remain fully immersed into the driving task and be ready at all times to perform the driving task. I’ve repeatedly warned about the dangers of this co-sharing arrangement and predicted it will produce many untoward results.

For my overall framework about AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/framework-ai-self-driving-driverless-cars-big-picture/

For the levels of self-driving cars, see my article: https://aitrends.com/selfdrivingcars/richter-scale-levels-self-driving-cars/

For why AI Level 5 self-driving cars are like a moonshot, see my article: https://aitrends.com/selfdrivingcars/self-driving-car-mother-ai-projects-moonshot/

For the dangers of co-sharing the driving task, see my article: https://aitrends.com/selfdrivingcars/human-back-up-drivers-for-ai-self-driving-cars/

Let’s focus herein on the true Level 5 self-driving car. Much of the comments apply to the less than Level 5 self-driving cars too, but the fully autonomous AI self-driving car will receive the most attention in this discussion.

Here’s the usual steps involved in the AI driving task:

  • Sensor data collection and interpretation
  • Sensor fusion
  • Virtual world model updating
  • AI action planning
  • Car controls command issuance

Another key aspect of AI self-driving cars is that they will be driving on our roadways in the midst of human driven cars too. There are some pundits of AI self-driving cars that continually refer to a utopian world in which there are only AI self-driving cars on the public roads. Currently there are about 250+ million conventional cars in the United States alone, and those cars are not going to magically disappear or become true Level 5 AI self-driving cars overnight.

Indeed, the use of human driven cars will last for many years, likely many decades, and the advent of AI self-driving cars will occur while there are still human driven cars on the roads. This is a crucial point since this means that the AI of self-driving cars needs to be able to contend with not just other AI self-driving cars, but also contend with human driven cars. It is easy to envision a simplistic and rather unrealistic world in which all AI self-driving cars are politely interacting with each other and being civil about roadway interactions. That’s not what is going to be happening for the foreseeable future. AI self-driving cars and human driven cars will need to be able to cope with each other.

For my article about the grand convergence that has led us to this moment in time, see: https://aitrends.com/selfdrivingcars/grand-convergence-explains-rise-self-driving-cars/

See my article about the ethical dilemmas facing AI self-driving cars: https://aitrends.com/selfdrivingcars/ethically-ambiguous-self-driving-cars/

For potential regulations about AI self-driving cars, see my article: https://aitrends.com/selfdrivingcars/assessing-federal-regulations-self-driving-cars-house-bill-passed/

For my predictions about AI self-driving cars for the 2020s, 2030s, and 2040s, see my article: https://aitrends.com/selfdrivingcars/gen-z-and-the-fate-of-ai-self-driving-cars/

Micro-Movements and the Development of AI Self-Driving Cars

Returning to the topic of micro-movements of human driving behavior, let’s consider how these actions come to play in the design and development of AI self-driving cars.

I usually get asked two questions about this topic, which comes up when I am speaking at industry conferences.

First, does it matter whether AI self-driving cars are developed such that they will be able to detect the micro-movements of other cars?

My short answer is that yes, it does matter and AI developers at the auto makers and tech firms should be including this capability into their AI self-driving cars.

Most of the auto makers and tech firms are not yet encompassing the detection of micro-movements. This lack of attention to the topic is due to several reasons.

Some of the auto makers and tech firms are oblivious to the nature of micro-movements and so it isn’t even a matter that has reached their awareness to do something about. Of those that are aware of it, they are already so taxed with trying to do straightforward driving tasks that trying to add the assessment and use of micro-movements in determining how to drive the AI self-driving car is an overload for them right now.

Indeed, many of those AI developers would contend that analyzing micro-movements and using those analyses for guiding the driving behavior of the AI is an edge or corner case. In the parlance of the AI field, anything judged to be a corner case or edge case is something that can be put on the backburner. It is not at the core of what they believe needs to be done. As such, they might consider micro-movements interesting, but not vital enough to warrant being on the pressing To-Do list.

I’d like to challenge that assumption about the low-priority assigned to dealing with micro-movements. I would argue that being able to detect and decipher the micro-movements of other drivers is a core driving skill. It allows you to anticipate what other cars and their drivers are potentially going to do. By knowing about micro-movements, seasoned human drivers can keep themselves out of potential troubles, which otherwise those human drivers that seem out-of-tune about micro-movements are more prone to getting caught off-guard by other drivers and their driving behaviors.

Some AI developers would say that detecting micro-movements won’t matter in a world of entirely AI self-driving cars, since the AI self-driving cars will presumably all be electronically communicating with each other via V2V (vehicle-to-vehicle) communications. As such, in my example of being at a red light and making a turn, if I was in an AI self-driving car it would have in-theory sent a message to the eager beaver AI self-driving car behind me, and warned it that my AI self-driving car might opt to surge ahead and yet might also then hit the brakes, all because of trying to gauge the traffic situation.

Yes, it is hopefully going to be the case that in an AI self-driving car world we’ll see that kind of V2V taking place, but meanwhile we are all living here in the real-world. As I earlier pointed out, for quite some time we are going to have a mix of human driven cars and AI self-driving cars. Likely a long time. We cannot decide now to avoid putting in place capabilities that involve dealing with human drivers. Pretending that everything will be fine once we get to an all AI self-driving car world, if we ever do, it’s living in a dream world. AI self-driving cars must be realistic and deal with a world of human driven cars.

If you take a look at many of the AI self-driving cars being tested on our roadways today, you can often figure out that they aren’t using micro-movements to their advantage. The stilted nature of the AI driving is a giveaway that the AI is not versed in detecting, analyzing, and making use of the “tell” of human drivers making micro-movements. In a sense, it is akin to a novice teenage driver that has not yet figured out how to “read” other cars and the driving behaviors of other drivers.

Now, there are some AI developers that claim they are worried that if they do include the detection and leveraging of human drivers’ micro-movements that the AI will then potentially get confused or make worse decisions.

Well, this really is more about how good or how poorly the AI development of this feature is undertaken. Sure, if you do a half-baked job of trying to leverage micro-movement detection, you might indeed have your AI self-driving car do worse than if you did things the right way. In my view, that would seem true about any of the capabilities you might opt to include into the AI system. Capabilities that are inadequately put together can be a burden and a problem, more so than an aid and a solution.

I didn’t say that adding the capability was going to be easy. In fact, I’ll readily state that it is hard.

Trying to use the sensors of an AI self-driving car to detect that the wheels of a car ahead are slightly askew, it’s not easy, and you need really good sensors and really good sensor processing software. The same can be said about detecting when nearly any of the typical micro-movements are undertaken. By definition, those movements are subtle. Picking up on the cues or tells of other cars is hard for other human drivers to do, and admittedly hard for an AI system to do.

For my article about AI developers’ overload: https://www.aitrends.com/selfdrivingcars/developer-burnout-and-ai-self-driving-cars/

For the role of internal AI naysayers, see my article: https://www.aitrends.com/selfdrivingcars/internal-naysayers-and-ai-self-driving-cars/

For how groupthink among AI developers can go awry, see: https://www.aitrends.com/selfdrivingcars/groupthink-dilemmas-for-developing-ai-self-driving-cars/

For the reverse engineering of AI self-driving cars, see my article: https://www.aitrends.com/selfdrivingcars/reverse-engineering-and-ai-self-driving-cars/

For more about edge or corner cases, see: https://www.aitrends.com/ai-insider/edge-problems-core-true-self-driving-cars-achieving-last-mile/

Training an AI Neural Network for Micro-Movement Driving Patterns 

Fortunately, you can leverage Machine Learning and Deep Learning to your advantage. By training a deep or large-scale multi-layer artificial neural network with tons of collected driving related data, based on human driven cars as they are driving in regular human driving traffic, there are patterns of the micro-movement driving that can be trained for.

These trained beforehand Machine Learning and Deep Learning algorithms can then be included into the on-board AI system of the self-driving car. In many respects, this is really just a deeper form of those capabilities. The odds are that Machine Learning and Deep Learning are already being used to examine sensory data for aspects such as street signs, the presence of pedestrians, etc. The micro-movements are essentially a step deeper into that kind of analysis.

As mentioned earlier, there are some tough aspects about trying to achieve the inclusion of the micro-movement analysis and usage capability. Will the cameras provide sufficient visual detection to spot the micro-movement? Will the radar provide sufficient detection? Will the LIDAR provide sufficient detection. Also, even if you can get the sensory data, what about the added computer processing needed to go deeply into the analysis, will that be on-board? Does that add cost, weight, heat, or other concerns in terms of providing the AI self-driving car with this capability?

You also need to realize that the micro-movement is not merely about detection. Once you’ve detected a micro-movement, it needs to be populated into the AI virtual world model. The AI action planner has to be then be versed in what to do with the indications about the micro-movements. If the AI action planner ignores the micro-movement detection, the whole effort is for naught.

The AI action planner also has to deal with micro-movements that might not be a true tell per se.

Recall that I earlier mentioned that in poker playing you might be mistaken about someone’s tell. Perhaps you thought that every time they rubbed their cards with their left hand that it meant they were bluffing, but maybe that’s not a consistent tell at all. Just because a car ahead of you might be leaning toward the bike lane, it doesn’t necessarily mean that the car driver is going to bust into the bike lane.

In this case, the AI needs to be able to include the micro-movements as part of a larger picture or “understanding” of what is happening in the traffic around the AI self-driving car. You cannot in isolation try to make use of the micro-movements. When you are behind another car, you might have several minutes to observe any of their micro-movements. By giving ample time to do so, their pattern of driving behavior is likely going to be more apparent than trying to make a snap judgement.

It is also important to realize that human drivers are going to try and spoof or prank AI self-driving cars. This has already happened in instances of four-way stops, involving human drivers that “intimidated” an AI self-driving car by rolling through the stop sign. The AI was trained to wait until the other drivers came to a full stop. It was the proper way to do things. Human drivers though aren’t necessarily “proper” about how they drive. In the four-way stop, the humans figured out that the AI self-driving car would remain still until the human driven cars had waited their turn, and so the human drivers just kept moving, essentially causing the AI self-driving car to freeze in place and continuously wait for its turn to go.

I mention this example of the four-way stop to also point out that the interpretation of micro-movements of human drivers could be a means for human drivers to trick the AI self-driving car. If you as a human driver know that the AI is going to watch the alignment of your wheels, you could do a kind of basketball-like head fake and shift the direction of your wheels. It might not be due to actually needing to turn the wheels, but instead a ploy to get the AI to perhaps grant you that sought for parking spot or otherwise give you an advantage over the AI system that’s driving the self-driving car.

Overall, I am an advocate of including the detection, analysis, and leveraging of human driving micro-movements for the AI to be able to do a better job of driving a self-driving car. It isn’t going to be easy to include this capability. It will though make for a more life-like AI driving system and one that since it is going to be immersed among human drivers could be considered an essential driving skill.

When I aided my children in learning to drive, I made sure to explicitly point out the micro-movement’s aspects. I’m sure that many parents don’t think about it and aren’t aware to bring it up for their novice driver children and assume that the kids will somehow just pick it up on their own. There are some AI developers that seem to think the same might happen for AI self-driving cars, namely that rather than trying to overtly train the AI to be mindful of micro-movements, those AI developers hope or assume that it will somehow emerge by the AI’s own divinity. I doubt it will.

For more about Machine Learning, see my article: https://www.aitrends.com/selfdrivingcars/machine-learning-benchmarks-and-ai-self-driving-cars/

For more about Deep Learning, see my article: https://www.aitrends.com/selfdrivingcars/ai-machine-child-deep-learning-the-case-of-ai-self-driving-cars/

For my article about the difficulties and goal of achieving one-shot learning, see: https://www.aitrends.com/selfdrivingcars/seeking-one-shot-machine-learning-the-case-of-ai-self-driving-cars/

For the aspects of egocentric AI developers, see: https://www.aitrends.com/selfdrivingcars/egocentric-design-and-ai-self-driving-cars/

For the use of probabilities in AI self-driving cars, see my article: https://www.aitrends.com/ai-insider/probabilistic-reasoning-ai-self-driving-cars/

I’ve tried to answer the first question which was whether or not AI self-driving cars should be outfitted with a capability to detect, analyze, and leverage the micro-movements made by human drivers. Of course, I said yes.

The second question that I get asked is whether the AI self-driving car should make use of micro-movements, doing so in a similar manner to how human drivers use micro-movements.

My answer is yes, the AI ought to also make use of micro-movements. Here’s why.

Human drivers are generally used to detecting the micro-movements of other cars. I realize that I’ve also given examples of human drivers that seem oblivious to detecting micro-movements, but those are a smaller percentage of drivers.

I would also claim that many drivers might not be consciously aware that they are being affected by the micro-movements of other drivers, and yet they are indeed being impacted. You might catch out of the corner of your eye, the subtle micro-movement of another car. Doing so, you might react without any recognizable or spoken aloud reason and respond to that micro-movement, nonetheless. Humans are quite good at doing pattern matching. They often are able to find patterns when they aren’t even thinking explicitly about doing so.

An AI self-driving car should be sending the same kind of subtle “signals” to human drivers, doing akin to what other human drivers do. This again is part of my overarching belief that if AI self-driving cars are going to be driving among human drivers, which we know to be the case, those AI drivers need to be doing actions that human drivers do.

If the AI lacks the micro-movement showcase capability, it means that those human drivers around the AI self-driving car will no longer have an essential “tell” that can forecast what the AI self-driving car is going to do. Without the tell, the human drivers are going to be caught unawares. What, that stupid darned AI self-driving car made a right turn, I wasn’t expecting it, will be a common complaint.

And in a manner of speaking, I’d say that those human drivers will be right. They will be well-justified in their complaining. Who wants to have other drivers (human or AI), for which those drivers do things seemingly without the usual warnings (or tells) that we already have grown accustomed to seeing? It’s a recipe for disaster.

Would you be willing to put onto our roadways a slew of drivers that aren’t driving as is customary? Not a good move.

If you did so, it would be like dividing the traffic into two classes of drivers. When you combine with this that the human drivers won’t readily be able to gauge what the AI driver is going to do, and if the AI driver isn’t also versed in reading micro-movements and therefore unaware of what the human driver is going to do, you are setting things up for a very messy and untoward outcome.

Conclusion

I sometimes liken the micro-movements topic to the nature of dancing. If two people are going to dance together, they need to figure out the subtle movements that indicate whether to turn to the left or turn to the right, and whether to speed-up or slow down the pace.

Each dancer needs to be able to detect the micro-movements of the other, along with knowing how to react.

Furthermore, each dancer needs to be able to exhibit their own micro-movements, in order to allow their dancing partner to detect the movement and be able to react accordingly.

Right now, we have humans that detect the micro-movements of other human drivers, plus human drivers emit micro-movements as part of their driving efforts (by-and-large). We are in the midst of witnessing AI self-driving cars being fielded and tested that don’t detect the micro-movements of other cars, and nor do the AI self-driving cars emit their own micro-movements.

You can’t have much of a fluid dance when one of the partners is clueless. In the case of cars, a dance partner that is clueless can lead to untoward car accidents. Human drivers won’t judge what the AI is going to do, and bang, their will be collisions.  AI self-driving cars won’t be able to well-judge what human drivers are going to do, and as a result the AI will drive in an overly timid and hesitant way, which will likely ultimately lead to car accidents.

It’s time to make sure that the dancing partners of human drivers and AI drivers are versed in the same kinds of capabilities and tactics of driving. I vote that the auto makers and tech firms put more effort toward the micro-movement elements, which makes sense to ensure that the dancing partners won’t collide into each other and make ghastly missteps.

Copyright 2019 Dr. Lance Eliot

This content is originally posted on AI Trends.