AI in Diabetes – A breakthrough in Healthcare

The National Diabetes Statistics Report, 2017, U.S. states that an estimated 30.3 million people of all ages or 9.4% of the U.S. population suffered from diabetes in 2015 and this count is growing every year.

These figures are alarming to healthcare authorities as well as to the controlling government. With an immediate attention required in this area, brilliant technologies like AI, machine learning and big data can be used to overcome the gap between those suffering and cured.

The economic cost to the US for diabetes care in 2017 alone amounted $327 Billion. This economic burden is getting out of the control with the number of diabetic patients adding every year.

For contributing to the cause, few digital health companies are taking the initiative to lessen this burden with the help of technology. They are leveraging technological advancements to innovate diabetes care solutions like non-invasive insulin delivery systems, continuous glucose monitoring devices, and digital diabetes management platforms.

These devices are the source of behavioral, physiological, and contextual data which can be analyzed and used to come up with more efficient diabetes care.

Today we are presenting some revolutionary startups who are making a contribution to help diabetes care evolve. Their contributions are remarkable with out of the box solutions for the problem at hand. Let’s take a look:

1) Livongo Health leveraging Big Data-Based Approach for Diabetes Care

Livongo Health is leveraging big data to help people manage their health conditions more efficiently and improve patient outcomes.

Hundreds of thousands of people are using their products such as blood glucose meters, blood pressure cuffs, and scales. The added advantage is that these devices collect data and send it to a larger database which is then used by the company for generating insights to benefit their members.

Also, this pattern has encouraged the startup to come up with a reinforcement learning platform where they observe the data and generate a variety of personalized messages to send to their members.

They learn about members’ behavior with the responses received and eventually know what works best for them. We would call this a good start!

2) Bigfoot Biomedical is working on AI-driven automated insulin delivery with an artificial pancreas

Bigfoot Biomedical, a California-based diabetes management company, is working on a mission to develop an automated insulin delivery system with an artificial pancreas. This system sounds promising and can make the lives of diabetic patients easy. The future of diabetes care will change with this product launched in the market.

The startup made it possible by leveraging the potential of AI to devise a closed-loop system that would observe and learn from the user’s response to food, exercise, insulin, and then adjust the dose.

A good head start is that the company has received a substantial financial support and thus the process of product development has fast-forwarded to the clinical trial phase.

It is just a matter of time that an AI-driven automated insulin supply system will become the life-changing diabetes care solution.

3) Glooko is providing mobile and web apps for diabetes care

Glooko is a global diabetes data management company which provides HIPAA-compliant and widely compatible mobile and web apps. These apps synchronize with diabetes care devices and activity trackers to collect data like insulin, blood pressure, blood glucose, diet, and weight.

The company is harnessing the power of Big data and predictive algorithms to empower diabetic care professional with tools to analyze trends and provide necessary recommendations.

Glooko collects data from over 180 exercise and diabetes care devices and then correlates it with exercise, food, medication, and other relevant data to deliver insights with clinical care and self-management.

These apps will contribute a lot to self-management and also sizeable improvement can be made in patient outcomes.

4) Virta Health is using AI to reverse Diabetes

Virta Health is a silicon valley startup which has embarked upon the mission to cater alternative treatment for type two diabetes without any surgery or medication. The company has already received 50% positive results in its clinical trial for reversing the chronic diabetic condition.

Virta has taken the nutrition centric approach which is based on ketogenic diet. With this diet, the body burns fat for fuel and not carbs. Virta has a user-friendly app which allows the user to enter ketones, blood sugar, and other relevant information.

Once the details are entered, the app uses AI to device a customized treatment plan for the individual.

Additionally, this app helps patients find specially assigned clinicians and health coaches for immediate assistance and consultation. Another great step in improving self-management!

5) Digital Diabetes Clinic by GlucoMe

GlucoMe is an Israeli startup which has invented a digital diabetic clinic which uses a cloud-based solution for remote diabetic care. With this facility, the healthcare professionals can remotely monitor the patient’s insulin and blood glucose and adjust the dose accordingly as and when necessary.

The data is transferred from smart glucose monitors and insulin pens to a mobile app which helps in monitoring and making decisions that support the platform to function.

AI and machine learning are used to generate meaningful insights and actionable treatment plans. The healthcare will be simplified to a great extent with the use of digital diabetic clinic.

Final words

Personalized treatment plans based on real-time data along with intelligent insulin delivery algorithms are the need of the hour. Technical advancement in the field of healthcare has a promising future and startup initiatives like these can open up a gamout of opportunities for healthcare professionals and patients.

Machine Learning Algorithm

There is one thing about the Machine Learning algorithm and that is there is no one approach or one solution that caters to all your problems. But you can always pick an algorithm that nearly solves your problems and then you can customize it to make it one perfect solution for your problem.

Here we are stating some factors that will help you narrow down your list of machine learning algorithm options.

But first things first, you need to have clarity of the data, your constraints, and your exact problem. For achieving clarity of data, do the following:

a) Know your data

To understand your data you need to look at summary statistics and try to point out the central tendency of data. For doing this, you will require to study the averages, medians, and correlation that indicates a strong relationship in data. The next thing to figure out is ‘what to do with outliers’. You can use box plots that can identify outliers. Apart from this, ‘clean your data’. Sort it for relevancy and segregate it on the basis of the problem at hand.

b) Categorize the problem

Once you know your data, you need to categorize your problem, which can be done in two steps:

• Categorize by input:

A supervised learning program is when the data is labeled. If the data in unlabelled and you desire to find an appropriate structure then it is an unsupervised learning program. One should know the type of inputs they can offer in order to choose an appropriate machine learning algorithm.

• Categorize by output.

Now, if the output of your model is in number form then it will be called a regression problem. If you desire classification of data as an output, it’s a classification problem. Another type of problem is clustering problem when the model required to set groups for the inputs given.

c) Find the available algorithms

After proper evaluation of your problems, you can opt to identify the applicable algorithms which are practical to implement using the available tools.

Most commonly used Machine Learning Algorithms

In this blog, we have listed out some of the commonly used Machine Learning Algorithms just to give you a heads up. Follow us for more intriguing updates on Machine Learning.

1. Linear Regression

This is the simplest Machine Learning algorithm. It can be used to compute continuous input data as compared to classification in which the output is categoric. In simple words, linear regression can be used to predict some future value of a process that is currently going on. It should be kept in mind that in case of multicollinearity the linear regressions are unstable.

Examples, where linear regression can be used, are:

• Predicting sales for the coming month
• The time required in commuting from one place to another

2. Logistic Regression

Logistic Regression can be used as a probabilistic framework or to incorporate more training data into the model in future. It is not just a black box method but it will help you to understand the factors behind the predictive outcome and so forth.

Examples, where logistic regression can be used, are:

• Fraud detection and credit scoring
• Estimating the effectiveness of marketing campaigns

3. Decision trees

Using decision trees alone is done very rarely. Usually, they are combined with others machine learning algorithm to build an efficient algorithm like Gradient Tree or Random Forest.

Examples, where decision trees can be used, are:

• Investment decisions
• Buy or build decisions
• Banks loan defaulters

4. K-means

K-means is used for the unlabelled data where the task is to cluster and label them. It is used when the user group is very large and you wish to categorize them on the basis of common attributes.

5. Principal component analysis (PCA)

The principal component analysis is used when the data has a high range of features and is highly correlated. In such a situation PCA will help you in dimension reduction.

6. Support Vector Machines

Support Vector Machine (SVM) is used on labeled data and is used widely in pattern recognition and classification problems when the input data has exactly two classes.

Examples, where SVM can be used, are:

• Text categorization
• Stock market predictions

7. Naive Bayes

Naive Bayes is based on Bayes’ theorem. It is a classification technique that is easy to build and works great with large datasets. It is a better classifier than discriminative models like logistic regression because it is quicker and requires less training data.  

Examples, where Naive Bayes can be used, are:

• Text classification
• To mark an email as spam or not
• Face recognition

8. Random Forest

Random Forest can solve both classification and regression problems on large data sets. Basically, it is a collection of decision trees. It is highly scalable to any number of dimensions and has usually quite acceptable performances.

Examples, where Random Forest can be used, are:

• Predict credit loan defaulters
• Predict patients with high health risks

9. Neural networks

Neural networks can be used to train extremely complex models and these models can be utilized as a black box. For example, object recognition is enormously enhanced by deep neural networks only.

Summing up

The above pointers will be a great help to shortlist a few algorithms but it is hard to figure out which algorithm will work best for your problem. Therefore, it is suggested to work iteratively. For picking the best one among the shortlisted alternatives, test the input data with all of them and at the end evaluate the performance of the algorithm.

Also, to develop a perfect solution to a real-life problem you need to be aware of rules and regulations, business demands, and stakeholders’ concerns and you should have considerable expertise in applied mathematics.

How Chatbots can help with Customer Service

The road to high sales goes via the street of customer experience. Customers desire a quick and easy solution to their problems. Hence, companies are expected to fulfill this demand.

The rise of chatbots has improved communications between businesses and brands. While customers leverage self-help, businesses can build stronger relationships and increase sales.

Here, in this article, you will find 3 fundamental ways how chatbots are improving customer service.

1. Responding quickly and engaging with customers

Businesses are avoiding delays in responding to customer’s requests. The goal for businesses is to resolve a query immediately. And a live chat assistant helps in achieving that goal. Customers are notified about the chat availability to resolve their queries at any time.

Wells Fargo uses a Facebook chatbot to resolve issues of their customers. Customers get to ask questions regarding credit cards, deposits, transactions and the locations of ATMs.

Similarly, the Bank of America has their own digital assistant. Customers are allowed to choose text or voice messages to ask their queries.

Chatbots use a pre-decide pathway of conversation. However, if the query is not according to the program, they can direct customers to the FAQ section of the platform. So, customers at least get a quick response to their queries.

Engaging customers is also an essential part of customer service. Experts think that chatbots can become operational in brand engagement as well. Engaging consumers allows them to become a customer of your business.

A consumer can learn about a brand or product during a chatbot conversation. Businesses are using chatbot to promote their updated products or new products. Whole Foods is using chatbot to provide upcoming recipes to their customers. A user can select an ingredient emoji and find recipes that include that ingredient.

2. Answering simple questions and reducing customer service cost

Most questions asked by customers are simple. But they all take time, which is why businesses are required to pay their service agents. Chatbots are capable of reducing that cost for good. There is no need for a large team of representatives. A single chatbot is enough to resolve simple queries from hundreds of customers.

Chatbots bring accuracy and cost-effectiveness to customer service. You can program a chatbot with simple questions and their responses. This way, customers get immediate engagement and the business gets to save money.

3. Being available 24/7 for customers

Every business presents customer service with continuous availability. A business that is available 24/7 is more reliable for customers. But with human representatives, you require a vast team to accomplish continuous assistance.

However, chatbots can stay available all the time. They stay active all the time and engage customers. Hence, you can have a small team of support staff and handle a vast group of customers.

Final words

If a customer is unsatisfied with your service, you can sell anything to him or her. In fact, you lose more customers due to bad service. That is when chatbot comes into the picture to improve customer service and enhance your ability to sell products and build a reputation.

AI in Real Estate Industry

The emergence of disruptive technologies isn’t a threat to human resources. Business ventures, tech biggies, and even small startups are considering Artificial Intelligence as a force to propel their ventures forward. Irrespective of the sector you are operating in, the superior consumer experience is of utmost importance. Especially, if you are offering a service, it would be highly imperative to ensure unparalleled and never-felt-before experiences for target customers.

The real estate scenario

With almost every sector embracing this technology with a lot of warmth, the real estate sector isn’t lagging behind too. Property companies and reputed realtors across the world have come forward and integrated AI systems into their existing infrastructure. Commercial, as well as residential buildings, are becoming smarter with each passing day. To be precise, the real estate sector is transforming and taking giant strides towards development. Here’s a quick look at how AI is making things happen:

The art of having smart buildings

Transforming data into actionable information is a crucial thing to do. Whether it’s a commercial building or a residential unit, AI will help you leverage available data sets and turn them into useful information. The following ways will define its role in ensuring smart buildings:

• Predictive energy utilization

AI-based systems have the power to identify the thermal characteristics of a particular building. You can identify temperature levels and energy consumption amounts thus optimizing smart energy utilization to a great extent.

• Fault detection

Artificial Intelligence can help you delve deep into the nuances of a constructional unit and identify the anomalies existing there. Timely fault detection leads to effective and preventive disaster management.

AI is revolutionizing real estate

Other than helping you transform a simple building into a smart residence, AI directly plays the instrumental role in promoting real estate. With data analysis, interactive chatbots, and machine learning, property developers, and buyers have loads of surprises in store.

1. Interactive bots

Some of the leading real-estate companies have intuitive and communicational bots who can provide 360-degree view of a particular property. Potential clients can have human interaction with these bots, ask questions, and streamline their search according to the suggestions.

2. Effective data analysis

Building IQ is one of those enterprising real estate firms who leverages AI for data analysis. The company captures the right amount of information about a building along with the avenues for improvement. This proves to be a significant move in reducing tenant costs and ensuring optimal comfort for them.

3. Optimum security

Secured living is the key to a happy life. Every individual wants to stay protected in their dream abodes and the security aspect of a building often proves to be a parameter for its saleability. With AI-enabled security systems, automated voice and facial recognition become quite easier.

4. Making recommendations

Whenever a new property enthusiast visits your site, you will have to provide the right information or suggestions that are useful to him. With AI-powered site management systems, you can identify the information that is useful to them and make valuable recommendations.

Signing off

AI can influence purchase decisions and help a real estate establishment earn huge ROIs. The discussion we had above will surely present a crystal clear picture in this context.

How Deep Learning is Personalizing the Internet for Better Engagement

Working as a secondary field of machine learning, Deep learning focuses on the interpretation of data. This technology uses multiple layers of processing to understand a set of data. Every layer allows machines to evaluate and present data in a meaningful manner. Hence, the performance capacity of algorithms increases with the increment in data.

Advanced techniques such as NLP and image classification become possible with deep learning. Businesses are using it to enhance their communication patterns with clients and customers. The successful use of such algorithms brings personalization to the communication and hence boost engagement.

Personalization in the internet-driven market

Publishing companies, marketing agencies, e-commerce and many other businesses work through the internet. Deep learning offers an error-free system to enhance engagement in such business models. This is beneficial for users as well as businesses.

Fueled by personalization, businesses can reach a higher level of productivity in terms of communication and online experience.

Here are most valuable ways, in which, deep learning is personalizing the online world.

1 – Making recommendations more effective and relevant

On the internet, sites recommend a different set of content to the site visitors. An e-commerce site does the same with their products. However, a visitor will engage with the recommended content or product if it is relevant to him or her.

Traditionally, metadata is used to help with recommendations. However, poor metadata quality always presented issues of content relevancy in this process.

Now, deep learning is bringing effective changes in this department. Advanced algorithms use intrinsic characteristics of a content and evaluate it with visitor’s interests. These intrinsic qualities can be text, images or videos, which changes from content to content. Such technologies are now capable of creating a comprehensive view of content on their own and incorporating it when recommending products or content pieces.

2 – Focusing on interests for new visitors

When a visitor comes to a platform, it seems difficult to learn preferences and understand behavior. This happens due to the lack of layered data evaluation. Deep learning algorithms divide the clutter of interactional and transactional data.

Rather than just focusing on data gathering, deep learning allows business platforms to understand and evaluate customers’ behavior. Hence, every visitor sees what he or she desires to see.

For example, if a customer visits an e-store for the first time to purchase an oven, algorithms will collect that data. However, the use of this data will depend on the interests of the same customer when visiting the second time. If the customer looks for a mobile phone, then, the algorithms will leverage that interest to present personalized data.

3 – Leading customers towards decisions

A customer goes through various steps before deciding to purchase a product or service. This decision depends on brand loyalty, personal goals, and preferences. Deep learning understands these factors and personalizes image and other forms of content. Hence, businesses get to increase sales.

Personalization is the key to winning the business game on the internet. And DL algorithms can offer strength to this goal of your business.

The Artificial Intelligence in Music Debate

Music has come a long way from the early days where the only instrument was the human vocal cord. The use of computer-based technology in music started in the 60s when the iconic Moog synthesizers starting taking over the British music scene and paved way to the irreplaceable artform that is progressive rock. If it was not for the Minimoog, Keith Emerson would have just stuck to literally rocking the Hammond L100, riding it like a bucking bronco, brutalizing it with knives and occasionally when he got the time even playing music on it. Yes indeed, the 60s and 70s were crazy times. But the introduction of integrated circuits and eventually processors paved way for a lot of innovation in music and the discovery of many new genres of music.

However, technology in music didn’t stop with the synthesizers. Over the past few decades, many technologies have been incorporated into music including the controversial auto-tune. In recent times though the most technology to make its way into the musician’s arsenal is Artificial Intelligence. While the overuse of technology in music is heavily debated and often shunned by the musicians of yore, there is still a lot of support as well. So, let us have a look at how AI is being used in the music industry today.

Algorithmic Composition

Surprisingly the roots of Artificial Intelligence in music date back to as early as 1965 when synth pioneer and inventor Ray Kurzweil showcased a piano piece create by a computer. Over the years, the use of computer programs in the composition, production, performance, and mass communication of music has allowed researchers and musicians to experiment and come up with newer technologies to suit their needs.

Algorithmic composition is one of the more complex applications of AI in music. Kurzweil’s early efforts towards producing music using computer algorithms relied on pattern recognition and recreation of the same in different combinations to create a new piece. The process of creating music cognitively used today have spawned from this idea. The algorithms used therein are created by analyzing certain human parameters that influence how music is perceived. The first step here is for the cognitive system to understand the music as a listener and then draw vital information from it as a musician would. To achieve this the AI system must first be able to compute the notational data in relation to its audio output. Aspects such as pitch, tone, intervals, rhythm, and timing are all taken into consideration here.

Based on the approach and the result intended there are several computational models for algorithmic composition such as mathematical models, knowledge-based systems, evolutionary methods etc. Each model has its own way of uploading a piece of music into the system, how the system would process it, what information it can derive from it and how it would do it. Composition is not always the purpose of an Algorithmic system. Musicians can use it for comprehension or even draw inspiration.

Non-Compositional Applications

While AI-based music composition is a work in progress that has been going on for decades and could take quite a few more to perfect, AI serves the music industry in many ways outside the studio.

Engagement Tracking

Today almost every form of art and entertainment profits most from the digital medium. This is also the case with music if it wasn’t obvious enough. Artists create music for a target audience which is spread around the many platforms like Youtube and Spotify. So, the engagement data recovered from these platforms have a huge impact on the songs that are created as well as how they are promoted. Artificial Intelligence plays a central role in this process. AI systems often act as the buffer between the distributors, advertisers, marketers and the audience for all their processes including monetization and the transactions involved therein.

Analytics

Analytics data is an essential aspect of almost every online venture today. And music is no exception. Music creators, independent and signed alike rely on analytics data to manage their online presence. AI-based analytics tools help bring speed and accuracy to the analytics processes, so as to help musicians keep up with the fast-paced internet competition.

It’s not Rock and Roll, Man

Music, being an art form that demands a great deal of creativity also demands a human mind behind it that is creative rather than analytical. This is the debate that has been surrounding the AI-music alliance for a long time. Technology, in general, has had the music world divided for eons. In a decade where bands like Pink Floyd thrived exclusively on ‘space age’ technology like the EMS VCS3 and a whole bunch of other stuff, acts like Rush, Motorhead and Aerosmith roughed it out with just the rudimentary instruments. Although most musicians are always open to new technologies or eventually warm up to them, that has not been the case with AI. The prospect of having to put in very less creative effort to compose may entice some, (especially given the volume of music that is being put out everyday) it also opens up the debate of industry giants such as Sony, Universal, Warner and Tips misusing or overusing it which could lead to an eventual vacuum in creativity.

I’m Perfect! Are You?

AI has always been a source of contradiction among all communities so it is no surprise that the music community is both supportive as well as skeptical of it. So far efforts towards harnessing Artificial Intelligence-based composition have been of infinitesimal proportions. So, there is not much out there for us to judge and take sides. Ventures like Artificial Intelligence Virtual Artist (AIVA) are working exclusively towards bringing out the full potential of AI as a means of completely automating music production. While such ideas are a grey area today, how these will come to influence music as we know it, only time will tell.

What you Should know about Google AutoML

One of the highlights of last year’s Google I/O was the announcement of Google’s Automated Machine Learning or AutoML which according to founder Sundar Pichai was their ‘AI Inception’. The Machine learning Cloud software suite is finally about to hit the alpha stage and as of now, it seems like there is a lot that developer and designers alike can leverage from this tool. So let us explore this new addition to the Google family.

What is AutoML?

AutoML is Google’s Cloud software suite for Machine Learning tools based on Google’s Neural Architecture Search (NAS). Using AutoML a user can train deep networks without having any expertise in deep learning or Artificial Intelligence. This is achieved by leveraging NAS to derive the most suitable data network for the task at hand. This is a huge step for businesses as they will be able to harness the power of machine learning without using experts with years of experience in any field of automation. Customization of various AI product models meant for mundane as well as complex tasks.

How Will Businesses be able to Leverage AutoML?

Today, with deep learning still being in its infancy, most business use, AI for simplistic tasks. And considering the fact that AI and Machine learning experts are hard to come by and are quite expensive to hire, most companies try to keep their deep learning efforts to a minimum. This is where AutoML makes a world of difference. One of the most common applications of deep learning for businesses today is Image processing. Today companies rely on Image classification networks that run on neural matrices. This although running on machine learning tools, requires quite a bit of manual effort to isolate the proper network for the image datasets and then train the models to adapt to the task they intend to accomplish. With AutoML companies can skip the tedious processes of research and get directly into the transfer process. As a matter of fact, Google’s first AutoML tool is intended for exactly this aspect for visual companies.

AutoML Vision

This is AutoML’s first tool which will help developers create custom image recognition models. According to Google this tool will allow developers to upload their labeled data and the system will automatically create a custom machine learning model for them. This, in turn, will allow them to focus more on product development, design and other important aspects.  Although still in its alpha stage, this tool is being used by many big-time players such as the animation giant Disney.

Conclusion

As the concept of ‘Software 2.0’ catches on, developers around the world are getting used to the new tools largely built upon their predecessors, to write software without actually having to code each and every part of it. Tools like AutoML are a good example of how this is being applied to every aspect of software irrespective of the purpose. With AI the scope is further enhanced. Yet, for such tools to achieve any degree of comprehensiveness, they themselves need to be further developed to accommodate the needs of the upcoming generations of developers who will be relying upon them to do most of the work. So the road ahead will be the chicken and egg paradox where the efforts of both the man and the machine will craft the future. Today we have that sense of direction as to where we need to focus our efforts in terms of research, which is great news for AI tools such as AutoML.

What You Need to Know About the New Google Assistant Update

As if we weren’t short of surprises this year particularly from the tech industry, Google made an announcement that could very well be the most pleasant one so far this year. At the Google I/O conference 2018 held this week, CEO Sundar Pichai announced that the Google Assistant will be getting major updates. And the updates are major indeed with several AI based application that could make life a ‘breeze’ for all you couch potatoes out there. Anyway, let us have a look at the updates that are soon going to hit the market.

Source: crowdfundingheadlines.com

Google Duplex

Speaking of couch potatoes, here is an update that is for the ages which could make that expression a literal one soon. The Google Duplex is an AI tool that will be assisting you with real world tasks all accomplished through the phone without you having to move an inch. With the ability to understand natural speech and complex sentences, the google assistant can now book appointments, reservations or orders and should any complication arise it will place a call to have a conversation with the concerned person and get it done. Which, while on the surface seems swell and all, but in the ‘real world’ when have we ever convinced a snooty receptionist at a fully booked hotel to make room. Maybe, Google should add the Jedi mind trick as part of their update if they haven’t already. ‘These are not the reservations you’re looking for’. Anyway, without digressing further, the application has to be trained in such conversations to become accustomed to and eventually become adept at it. And Google stresses that the application can only carry out conversations based on such scenarios and not answer questions like ‘why won’t my girlfriend talk to me anymore? I only made an observation about her dress’. For that, you still have to go to Quora.com.

Enhanced Interface

Google is also planning on changing the interface of the Google assistant on both iOS and Android platforms to make it more informative and interactive. A certain degree of automation will be available in the interface as well with timeline-based snapshots, suggestions, summaries of activities etc. being part of the order. All your planner based details from various applications will also be integrated to the assistant. As announced earlier this year Google’s Smart Displays will also be launched later this year which will further enhance the experience.

Google Maps

Google maps will now be integrated with the Google Assistant AI to allow multitasking. Now you can access weather-related information, songs, send text messages and even podcasts for some reason according to news outlets. This raises an interesting question, what are Google’s policies on driving while speaking on the phone? Because, hands-free or not, talking on the phone is something that I assume is frowned upon all the world. As a matter of fact all over India that is an offense punishable by law. So, food for thought?

New Voices

One of the most highlighted features of this update are the 6 new male and female voices. And the most highlighted aspect of these voices is that they are ‘natural sounding’. So instead of that persistent and seemingly strict lady asking you to take a left turn at 100 meters, you get John Legend and no you won’t be getting ‘all of him’, just his voice. Hey, at least now we can have a soothing semi-baritone voice telling ‘All your base are now belong to us’ when the machines take over. And if you think I am exaggerating, John legend himself feels threatened by his AI self. That’s why he felt the need to tweet out ‘Real John Legend > AI John.’ Anyway, the new voices will be playing a huge role in achieving the goals that Google seeks to achieve through Google Duplex. Furthermore, you will no longer have to use the phrases ‘OK Google’ or ‘Hey Google’ to trigger a conversation with the assistant. The new update will enable the assistant to distinguish conversations with others from the ones you make with it. Which again is not necessarily a reassuring prospect.

To sum up the new updates seem to be targeted at enhancing user experience by a huge margin and resolving some of the most infuriating issues people have had over the years with various google assistant functions. As ambitious as it is, the project will take a while to incorporate all these functions and hopefully, it would change our lives for the better.  And the question in everyone’s mind now probably is what is going to happen with Siri?

A Brief Look at the Role of Artificial Intelligence in Logistics

Modern businesses rely a lot upon a good logistics system. Keeping up with the sheer volume of business that companies deal with today doesn’t make things any easier. This is where all the automated goodness of AI plays a major role as a disruptor. Given the expanse of functions within the logistics industry, it is no doubt that there is a lot that AI could do and to a great extent is already doing.

For the logistics industry, the journey has been long and eventful, starting in the early days of pioneers looking for trade routes to procure tea and spices. At this juncture of the 21st century, however, logistics has evolved to its fullest as far as infrastructure is concerned. Furthermore, there are very few industries today that does not apply logistics in one form or the other. There are quite a few aspects of logistics that AI could be applied to. Here are a few areas where artificial intelligence is applied in logistics.

Automating Transport Management

The heart and soul of the logistics industry is transport. Without transport, every other process in logistics is just redundant. While the 20th century could make do with paper and ledger based management systems, transport in the 21st century requires a giant leap. This is where AI’s role is pretty cut and dry. Before getting into AI’s role in transport management, we need to address the current scenario surrounding this technology. Transport being a rather physical function, most logistics companies address it as such and consider it quite a priority and with good reason. There are several hassles and risks involved with transportation which go beyond the pen and paper strategies devised in offices. This is the reason why most companies leave the data based technological bits to third-party logistics companies or 3PLs. This has slowed down the widespread adoption of technology in general.

This trend of outsourcing technological details to 3PLs has also limited the influence of mobile technology which plays a huge role in AI-based applications. So, coming back to automation, the advancements made in optimizing transport management has been monumental. As far as AI in transport management is concerned, the idea is to automate as much of the entire process as possible. And to this end, there are quite a few remarkable devices and applications out there.

1. Live Tracking

The fast pace at which the world moves today demands fast systems for anything and everything to cope with our lifestyle. In logistics live tracking has been quite a disruptor. There are several automated tracking applications for all platform that collect and relay a lot of vital data. From scheduling trips to providing live updates of the cargo’s position, AI-based applications ensure an open and intuitive line of communication between the bases and mobile units. Even information such as traffic, potential delays and so on are relayed using predictive analysis. Furthermore, advancements in the mobile industry and the level of simplicity they have facilitated therein have inspired various businesses to onshore many of their processes pertaining to this. Online retail companies such as eBay, Amazon and so on have already adopted this technology on a rather comprehensive level with their management capabilities going down to the door level.

2. Advanced Black Boxes

Black boxes in trucks have received an overhaul as well. Advanced back boxes provide a great deal of support for the operating companies as well as drivers. These devices are capable of collecting information such as engine temperature, the status of the load, tire pressure and so on. Despite their fundamental purpose as a disaster recording tool, these black boxes make this data analyzable in real time and allow operators to monitor and manage their transport systems well. Similar advancements have been made in the systems of cargo ships and airplanes along with advanced automated navigational tools.

WorkFlow and Process Automation

While transport is a major aspect of logistics, it is still just a part of a larger process comprised of several smaller processes. These are the processes where AI could manifest into more cognitive and at times even physical forms. There are various manual processes which take thousands of man-hours to get through, and after all that could still yield various levels of errors. Here are a few of those processes that have been optimized by AI.

1. Warehouse Management

Ask anyone who has worked in a warehouse and they will tell you how hectic the schedule there is. The volume of loading and unloading that take place alone is enough to drive Rain Man and John Nash crazy. Loading and unloading then again are just the tip of the iceberg. AI in warehouse management has simplified many of these processes. The use of AI-based sorting and labeling systems have allowed logistics companies to speed up these processes and minimized the margin of error.

2. Scheduling

The AI systems in logistics today are also capable of scheduling various tasks within the entire process. Automation of planning and scheduling is something that has revolutionized logistics. AI systems, based on various warehouse itinerary data schedule the transportation, organize pipelines for cargos, assign and manage various employees to particular stations and so on. Apart from the obvious con of potential layoffs, this particular application of AI in warehouses has facilitated a faster network for logistics processes.

3. Robots

While AI’s application in logistics has been in a minimalistic way to various mundane tasks, one of the most pronounced applications has been that of robots. Despite the constant opposition from various scholars and trade unions, robotics has been applied to a great extent in all fields and logistics has not been spared in the least. From working in warehouses to transferring freight at cargo airports, robots have their work cut out for them and they are doing it at speeds their flesh and blood counterparts could never achieve.

Conclusion

Despite the slow rate of progress in adopting AI as a logistics tool, the technology is slowly but surely seeping into the system. Machine learning has further allowed investors with a broader outlook on how AI could revolutionize logistics just as it has done in many other verticals. The issue that surrounds AI now is not lack of optimization but the dilemma of having to divert time towards setting it up. This in today’s context makes sense as most companies shy away from investing in newer technologies unless a dire need for it is presented.