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Brace Yourself for Multimodal Learning: AI’s Next Big Surprise

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Artificial Intelligence

Today, technology is moving at a lightning-fast speed. We have more power in our pockets than we ever had. Even as we take a look around, we find ourselves surrounded by technology. Be it business, research, healthcare, education or any other walk of life; people are using technology to get ahead in the race. What was once a dream technology has helped it turn into a reality now. We are observing a similar scenario for artificial intelligence. Even though we’ve always seen artificial intelligence as a fascinating concept, it is phenomenal to see it blend into our lives so naturally. 

Artificial Intelligence is paving the way for a wide variety of advancements in society. Scientists are making a breakthrough in AI’s subdomain of machine learning and developing a computer model that works in a way like the neurons in a human brain. Popularly known as the neural networks, these models have helped us accomplish complex functions and find answers to some of the fundamental issues around us. For example, face recognition is possible because of neural networks.

Artificial Intelligence

Artificial Intelligence

Similarly, other elements are helping artificial intelligence and machine learning to attain a boost. One such technology is big data. Nothing is denying that fact that in today’s world, we are surrounded by data. No matter where you look, it’s evident that digitization has swept the world off its feet. Everything we know has either turned online or is in the process of digitizing. With this and cloud to the rescue, it has become more than ever convenient to track every moment and gather data. Ultimately, it has powered artificial intelligence and helped it achieve a higher level of integration in our day to day lives.

As far as integration is concerned, there is a lot of research going on in the world is helping objects around us connect with AI and increase our user experience. Be it visual or sound; things are connecting in our surroundings and unifying for the greater good. Believe it or not, but today there are petabytes of data flowing through the artificial intelligence systems all across the globe. However, there is one challenge prevailing with these AI systems. With the abundance of data, the new AI devices need to learn, think and work together. As easy as it sounds, it is much difficult to achieve this in practice. Although companies are striving hard to incorporate multiple learning abilities in one system, we still have a long way to go. 

According to recent research, the total installed base of devices will grow from 2.7 billion in the year 2019 to a whopping 4.5 billion by the year 2024. This huge number will not just be satisfied with the current level of expertise we have in artificial intelligence. The growing demand calls for a much greater customer experience that could only be achieved when AI devices learn to remove any gaps between the data. We can see all AI devices working independently of each other today. But, the future calls for an integrated system where a high volume of data flows seamlessly through multiple devices. According to research, there is only one way to achieve this. And it’s called multimodal learning.

Multimodal Data

To understand multimodal learning, we need to take a step back from technology and perceive the world in a more natural sense. Take a look around you. We don’t just see things or hear things exclusively. We do all of that at the same time.

In other words, our experience of the world around us is multimodal. We see things, hear various sounds, taste flavours and at the same time, smell odours. We can do all of this because of our innate ability to experience things. Put differently; we are born this way. Modes are just different channels of information or any element that communicates meaning in some way. These include pictures, illustrations, audio, speech, writing, music, movement and gestures, colours etc. 

Modality thus refers to how a thing happens or occurs. Hearing, watching, smelling, tasting are all different kinds of modalities that we as humans experience. 

For a research problem, a multimodal challenge would mean the kind of data involving multiple modalities. Just as multimodal data is fundamental to the human experience, it must be the accepted organically by the artificial intelligent devices to work in the coming generation. 

In a world where almost everything around is multimodal, AI needs to make progress and must be able to interpret the data phenomenally. For example, take the case of images. Images are associated with tags and text explanations or captions. While tags help in identifying different people, the text helps in understanding the image more appropriately. Similarly, there are different modalities characterized by unique statistical properties. 

What is Multimodal Learning?

Multimodal learning is one of the most fast-paced models with the potential to transform artificial intelligence in the future. Stuart Carlaw, Chief Research Officer at ABI Research, explains that multimodal learning helps in consolidating disconnected and heterogeneous data from various sensors and data inputs into a single model. 

A unimodal system, on the other hand, cannot achieve a robust inference or even new insights since they are unable to combine signals from different modalities. Multimodal systems can effortlessly carry complementary information about different elements. This practice becomes evident when both of these are included in the learning process.  

The multimodal system solves the problem of analyzing data separately from different systems and then concluding it. It can organically process multiple datasets using learning-based methods to generate more intelligent insights. Multimodal learning serves two main benefits. One advantage is that it helps in predictions, and the other is that it draws more robust inference than other learning models. Let’s take a look at them.

Predictions are one of the most fundamental tasks of an artificial intelligence system. You give it some data from the past and present, and it gives you perfect insights into the future. Predictions based on AI are being used in all walks of life, be it to monitor customer’s buying patterns or save lives in the healthcare industry. However, we’ve been doing it with unimodal systems until now. With multimodal scenario in the picture to observe the same phenomenon, we can make more fierce predictions by detecting even the smallest of changes arising on account of multiple modes.

Drawing inferences is crucial to identifying the success or failure of a system. However, different modes of data might not be able to offer insightful inferences when viewed individually. On the other hand, they might be causing an impact on each other. Unimodal systems in artificial intelligence usually miss out on such information. And that’s when multimodal learning comes into the picture. They allow the capturing of complementary information by fusing multiple sensor modalities. For example, a trend might not be visible just from knowing the last purchase date of a customer. However, if you also take into account what they bought during a particular season, the entire data would start making more sense. 

Multimodal for Entirety of Operations

A majority of organizations in today’s world use only unimodal learning approach. But there are a few organizations like IBM Watson and Microsoft Azure that are well-known for using multimodal platforms. 

With only a few key players who are exploring multimodal learning and advancing towards the future of artificial intelligence, there is a huge gap between demand and supply. Be it inferences or predictions; if the world wants to develop a deeper understanding using artificial intelligence, it needs to step up with multimodal learning. 

Given the rise in demand, the multimodal approach has a great many opportunities to scale its operations. The underlying components or supporting technologies such as deep neural networks have already been utilized to a great extent in the world today. The examples of it include natural language processing and voice recognition systems in assistants like Alexa or face recognition in camera surveillance. 

Furthermore, as the competition is becoming more and more fierce in the world today, the cost of building new hardware sensors and perception software is falling rapidly. All these factors are making multimodal more scalable and approachable, ultimately allowing new companies to jump in the domain. 

Even organizations around the world are recognizing the importance of multimodal for developing an ecosystem where there exists an entirety of operations. They are eager to break out of the monotonous AI results by investing in multimodal learning.

Statistics suggest that multimodal applications will grow to a whopping 514.2 million by the year 2023. The compound CAGR, when calculated, turns out to be an astonishing 83 percent. Even though IBM Watson and Microsoft Azure exist as two of the key multimodal platforms solutions, they have failed to garner the required traction from users. It may be due to the poor marketing and positioning of the multimodal system’s capabilities. 

This point leads us to believe that there is not just a lack of supply in the market but also a lack of strategic planning around it. As a result, there are plenty of opportunities for platform companies to come and explore multimodal learning.

Use Cases

The utility of multimodal learning spans across multiple industries. For the starters, it is extensively used in the automotive industry, advanced driver assistance systems, in-vehicle human-machine interface assistants, driver monitoring systems and plenty of others. 

Research indicates that robotics companies are using multimodal systems in their Human Machine Interfaces and movement automation. Similarly, the companies dealing with customers are finding multimodal systems to be of use in security and payment authentication, recommendations, personalization engines on websites, along with personal assistants. 

The healthcare industry is also willing to adopt a multimodal approach for medical imaging. You can also find multimodal learning being taken up by the media and entertainment industry for enhanced content structuring, personal advertising, automated compliance marketing, among others. 

The Need for Multimodal Learning

Even though the world is exploring multimodal learning of late, it research in the field of computer science began way back in the mid-1970s. But ever since researchers applied the concept of deep neural networks to it, the niche has taken a giant leap forward. 

Since then, multimodal learning has made complementary data streams of different modes more prominent and interpretable. Owing to this, business applications are now falling into three broad categories of classification, decision making and the Human Machine Interfaces. Let’s take a brief look at these-

Classification: Multimodal training is giving developers the capability to classify data in a way that would have been impossible in a unimodal system. Moreover, the entire process around classification becomes complex when left to employees. A multimodal system, on the other hand, helps in automating, simplifying and improving the quality of data classification. 

Decision Making: Just like classification, multimodal systems can be used for making better decisions. It helps in predicting the best course of action, in response to a current situation or unfolding events in the future because there are more insights on the table. As multiple modes of data or modalities are combined right from the training process of a decision-making system, it becomes convenient to come up with a decision that is drawn by considering a wide variety of parameters. 

Conclusion

The field of multimodal learning might be in its nascent stages right now. But as we move towards exploring the future of artificial intelligence, we find it reaching its full potential. Today, you can find the most extensive application of multimodal learning in language modelling of smartphones. But to help cultivate it into a full-fledged evolved technology like .net development, stronger efforts are required. Companies must focus on planning and marketing multimodal solution well. With current trends and a few measures, it won’t be long when artificial intelligence gets more intelligence.

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One may think that successful machinery transport is just as easy as shipping other items. Though moving them from one place to another indeed follows the same shipping principles applied globally, there are still some exceptions, especially for some heavy machinery. With that being said, any organization needs to be aware of the important practices that encompass machinery shipping. Machinery Transport 101: How to Transport Machinery and Their Parts Successfully shipping any type of machinery and its parts require individuals to determine the proper mode, carrier, and packaging needed to transport such materiel. The following practices always take precedence when transporting such assets: • Apperceiving the machinery for transport. An initial assessment of the machinery to be transported must be completed before the transport itself takes place. The clients must take note of any features that could affect freight capacities like weight and dimensions. The key here is to understand the type of machinery that needs to be shipped. Researching how other clients and carriers have moved them in the past can ensure a shipment’s success. • Packing the materiel accordingly. Transporting heavy machinery cannot be compared with regular freight movement where small items are packed in boxes filled with protective material such as bubble wrap. It often brings with it more machinery shipping packaging regulations compared to regular transport. Planning and proper handling of heavy machinery are indeed needed. There is a delicate balance to maintain between a client’s shipping needs and the carrier’s packaging requirements. Some carriers have strict regulations, requiring every machinery fit for transport to be stored in container vans, which adds rental costs to the client’s expenses. Aside from that, knowing the kind of work needed to load the equipment is necessary as well. For instance, loading docks and cranes might be needed to get heavy equipment on the back of a trailer truck. • Determining the Suitable Mode. Knowing the difference between FTL and LTL is important for determining the appropriate mode to use. FTL (full-truckload) service means that an entire truck is necessary to fully transport the cargo, while LTL (less-than-truckload) means that the shipment can be moved using a relatively smaller vessel. With that said, clients must consider their preferred mode for transporting their machinery. • Selecting the “Right” Carrier. Now, this one is a combination of what the sender currently knows about shipping machinery and the most relevant carrier available to them. Depending on the type of machinery a client or a company wants to ship, finding the right freight movement company could prove to be difficult. Regardless, the aim is to find a carrier that’s specialized and known for reliable machinery transport and especially the particular machinery they need transported. For instance, shipping heavy machinery would be handled better by international carriers who specialize in the movement of heavy machinery like bulldozers, backhoe loaders, heavy hydraulics, etc. The efficiency and rate of shipping success increase exponentially with these specialized carriers, not to mention they also reduce the risk of unanticipated costs and damages. Knowing the Different Shipping Modes Since we’ve previously talked a bit about the shipping modes, let’s go through all of them and compare what each can provide clients. It’s vital to know about the various shipping modes because they’re all dependent on timing, cost, and size of the shipment. Taking the time to read through each mode would help you decide which one is the most suitable in your case. • Full Truckload (FTL) – This mode necessitates the use of a whole trailer, with the shipment’s overall weight reaching over 6,804 kilograms (15,000 pounds). • Less than Truckload (LTL) – This mode is used when the shipment weighs between 68 (150 pounds) and 6,804 kilograms but doesn’t need a full trailer. • Partial Truckload (PTL) – This mode is used when the shipment’s weight is over 2,268 kilograms (5,000 pounds). However, you may need to split the expenses of a truck with other clients. • Intermodal – This mode is used when there’s a need (or preference) for combining various transportation modes such as rails, trucks, or ships. One could say that this works best for reliable machinery transport overseas, in vast territories such as Australia. • Expedited – This mode is used when time is of the essence for shipping heavy machinery. Keep in the mind that additional costs may arise from selecting this mode. • Flatbed – This mode is used when the shipment must be loaded from the side or needs dimensional flexibility. Evaluate the Machinery’s Extent of Liability In every shipment that carriers make and every mode of transport, there is a limited liability coverage set for moving all kinds of freight. Both the client and the freight service must come to an agreement to evaluate this extent. The liability coverage is determined by the carrier and is reliant on the type of commodity to be moved. For example, LTL covers a fixed amount of payment per kilogram of shipped freight. Keep in mind that understanding a carrier’s liability for loss of cargo and the limits of their liability coverage offers are good practice. It helps you get a better picture of how they run things and if they’re the right carrier for you. Now that you have an idea how machinery transport and how important practices in general freight shipment work, you can now select the right international transport service for you, to effectively increase your chances of your machinery being shipped successfully. Remember, successful freight movement isn’t just up to the carrier; clients have a very important role to play too.
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