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Artificial Intelligence has accelerated the world of the healthcare industry. It is influential in improving diagnostics tools, interacting better with patients recovering from surgery or with mental illness, transporting medical samples and medicines. And now it is proving its mettle in drug discovery too. From target discovery to adaptive clinical trial design, AI has come a long way. Besides cutting time, it has also been key to identifying numerous compounds that has the potential of treating or preventing diseases. A traditional approach would have taken lots of expenses and development time without any guarantee of success. Getting a single drug to market consumed a shocking period of 10 to 12 years, with an estimated price tag of nearly US$2.9 billion. It’s no surprise that scientists in pharmaceuticals and biotech companies are looking for alternative ways to increase efficiency.

Artificial Intelligence has accelerated the world of the healthcare industry. It is influential in improving diagnostics tools, interacting better with patients recovering from surgery or with mental illness, transporting medical samples and medicines. And now it is proving its mettle in drug discovery too. From target discovery to adaptive clinical trial design, AI has come a long way. Besides cutting time, it has also been key to identifying numerous compounds that has the potential of treating or preventing diseases. A traditional approach would have taken lots of expenses and development time without any guarantee of success. Getting a single drug to market consumed a shocking period of 10 to 12 years, with an estimated price tag of nearly US$2.9 billion. It’s no surprise that scientists in pharmaceuticals and biotech companies are looking for alternative ways to increase efficiency. AI is an imperative asset as it is an annotator for clinical data. Almost two-thirds of the healthcare data is ambiguously structured. More data more is the demand for computation approaches and empirical shortcuts. With the help of natural language processing, AI can quickly explore and sift through arrays of this unstructured data to read, understand and categorize them. It can utilize algorithms, heuristics, and pattern matching to figure out physico-chemical insights that can qualify for the discovery of new compounds for medical purposes. Or it can use historical pieces of evidence to predict the possibility of a compound that shares similarities with the hypothetical ones scientists are looking for. Because of this ability, AI enables drug discovery teams to be far more focused and efficient. When these promising benefits of AI are coupled with avant-garde automation technology, humans will achieve limitless prospective applications. The automation market brings diverse options of tools for the bio-pharmacy community. Considering the immense expenditure on R&D, it enables the lifting of blockages that occur in many processes downstream to target identification and screening. In other words, it reduces late-stage compound drug rejections. Further, it helps in carrying out repetitive and menial tasks like picking and placing sample vials, labeling, etc… Thus saving skilled workforce hours and gives better financial returns. Along with AI systems, introducing the automation process can improve a design hypothesis for a drug through feedback analysis. Now it is possible to have a fully automated multi-step and parallel synthesis of highly complex molecules at ratios from nanograms to grams. This can potentially fast track time frames for compound discovery and optimization and enable more functional and iterative searches of chemical space. If one thinks of AI most benignly, it helps us to streamline the exploding data from various outputs. By making the data comprehensive, it enables researchers to come up with better solutions. However, not everyone is welcoming this transition as they fear redundancies in the healthcare sector and isolation among lab workers. While it minimizes human error, a single minor glitch can cause horrible repercussions. Therefore one must be careful in defining and refining the program codes and use them as the requirement. Also since now man is the master of the machines and can predict their outcome, he must take this leap of faith over risks. As automation led AI is spearheading the medical world, pharmaceutical firms are showing increased hunger for data. Paired with customized automation can allow compatibility, configurability, and flexibility with other resources. Thus bringing good ROI for drug research companies and step up the productivity to meet the rising market demands.

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How Is Automation Transforming The Travel Industry In 2023?

The Travel Industry is also undergoing rapid transformation. To improve client encounters Let’s look at some of the ways automation is changing the travel Industry in 2023

Booking: Artificial Intelligence and Machine Learning algorithms are being used to develop more efficient and accurate travel scheduling systems capable of suggesting the best bargains on flights and accommodations as well as personalizing travel plans.

Fraud Detection: Artificial Intelligence and machine learning can also be used to identify and avoid fraud. Algorithms are taught on big data sets to identify patterns indicative of fraud, such as anomalies in trip reservations, payments, or consumer behavior.

Language Translation: With the worldwide increase in cross-border travel, automation technologies such as AI-powered language translation are becoming essential for travel companies and online platforms serving clients from various countries.

Chatbots & Virtual Assistants: Chatbots and virtual assistants are being used extensively in the industry to provide 24/7 customer assistance, answer commonly asked inquiries, and assist with the booking system. They also help with post-booking tasks like altering or canceling bookings. This not only provides customers convenience but also frees up human customer assistance to tackle more complicated inquiries.

Transportation and Supply Chain: Another significant field of the travel business where automation is making a difference in supply chain and logistics administration. Automation improves routes, controls supply, and even forecasts demand. This assists travel businesses in lowering expenses and increasing efficiency.

Hospitality: The hotel business has begun to use robots for housekeeping, cleaning, and delivery, lowering personnel costs and improving productivity. Some of the world’s most famous hotel companies use robots for room service and room administration, such as managing the lights and climate. Robots are also lowering visitor wait times by speeding up the check-in and check-out processes. These are just a few instances of the front counter and staff administration that robotic automation makes feasible.

Rental car industry: The leasing procedure is being streamlined by automation. Self-service kiosks and smartphone applications, for example, are now available in the rental vehicle business for faster check-in and check-out. GPS monitoring and automated vehicle maintenance systems are two more examples of how automation is assisting rental car businesses in improving fleet administration.

Cruise industry: Automation in tracking, propulsion, freight management, and other areas is increasing ship productivity and safety.

Rail industry: Automation has greatly improved the Train Control Technique, Train Management, Train Maintenance, Passenger Information System, ticketing and fee collection, Train Dispatch System, Safety systems, and many other areas in this business.

These are only some instances. There are numerous areas where mechanization is bringing about change through newer and better equipment and altering the way we journey. ChatGPT, the newest AI invention, is heralding the beginning of an age of automation as we speak. ChatGPT can be easily incorporated to raise automation to new heights due to its ability to handle a wide range of activities, from chats to various other applications. ChatGPT’s power is undeniable, and it has the ability to change the game in the business. While the potential of these tools can be both motivating and frightening, when used properly, computerized systems can bring about new breakthroughs and benefits to humankind in a variety of fields, not just the tourism industry. ChatGPT and other extremely complex AI models may be at the forefront of AI-enabled automation in the future.

What Is Generative Ai And Why Is It Important?

Definition: What is Generative AI?

As the name suggests, Generative AI means a type of AI technology that can generate new content based on the data it has been trained on. It can generate texts, images, audio, videos, and synthetic data. Generative AI can produce a wide range of outputs based on user input or what we call “prompts“. Generative AI is basically a subfield of machine learning that can create new data from a given dataset.

If the model has been trained on large volumes of text, it can produce new combinations of natural-sounding texts. The larger the data, the better will be the output. If the dataset has been cleaned prior to training, you are likely to get a nuanced response.

OpenAI Playground

Similarly, if you have trained a model with a large corpus of images with image tagging, captions, and lots of visual examples, the AI model can learn from these examples and perform image classification and generation. This sophisticated system of AI programmed to learn from examples is called a neural network.

At present, GPT models have gotten popular after the release of GPT-4/3.5 (ChatGPT), PaLM 2 (Google Bard), GPT-3 (DALL – E), LLaMA (Meta), Stable Diffusion, and others. All of these user-friendly AI interfaces are built on the Transformer architecture. So in this explainer, we are going to mainly focus on Generative AI and GPT (Generative Pretrained Transformer).

What Are the Different Types of Generative AI Models?

Amongst all the Generative AI models, GPT is favored by many, but let’s start with GAN (Generative Adversarial Network). In this architecture, two parallel networks are trained, of which one is used to generate content (called generator) and the other one evaluates the generated content (called discriminator).

Basically, the aim is to pit two neural networks against each other to produce results that mirror real data. GAN-based models have been mostly used for image-generation tasks.

GAN (Generative Adversarial Network) / Source: Google

Next up, we have the Variational Autoencoder (VAE), which involves the process of encoding, learning, decoding, and generating content. For example, if you have an image of a dog, it describes the scene like color, size, ears, and more, and then learns what kind of characteristics a dog has. After that, it recreates a rough image using key points giving a simplified image. Finally, it generates the final image after adding more variety and nuances.

What Is a Generative Pretrained Transformer (GPT) Model

Google subsequently released the BERT model (Bidirectional Encoder Representations from Transformers) in 2023 implementing the Transformer architecture. At the same time, OpenAI released its first GPT-1 model based on the Transformer architecture.

Source: Marxav /

So what was the key ingredient in the Transformer architecture that made it a favorite for Generative AI? As the paper is rightly titled, it introduced self-attention, which was missing in earlier neural network architectures. What this means is that it basically predicts the next word in a sentence using a method called Transformer. It pays close attention to neighboring words to understand the context and establish a relationship between words.

Through this process, the Transformer develops a reasonable understanding of the language and uses this knowledge to predict the next word reliably. This whole process is called the Attention mechanism. That said, keep in mind that LLMs are contemptuously called Stochastic Parrots (Bender, Gebru, et al., 2023) because the model is simply mimicking random words based on probabilistic decisions and patterns it has learned. It does not determine the next word based on logic and does not have any genuine understanding of the text.

How Google and OpenAI Approach Generative AI?

Both Google and OpenAI are using Transformer-based models in Google Bard and ChatGPT, respectively. However, there are some key differences in the approach. Google’s latest PaLM 2 model uses a bidirectional encoder (self-attention mechanism and a feed-forward neural network), which means it weighs in all surrounding words. It essentially tries to understand the context of the sentence and then generates all words at once. Google’s approach is to essentially predict the missing words in a given context.

Google Bard

In contrast, OpenAI’s ChatGPT leverages the Transformer architecture to predict the next word in a sequence – from left to right. It’s a unidirectional model designed to generate coherent sentences. It continues the prediction until it has generated a complete sentence or a paragraph. Perhaps, that’s the reason Google Bard is able to generate texts much faster than ChatGPT. Nevertheless, both models rely on the Transformer architecture at their core to offer Generative AI frontends.

Applications of Generative AI

We all know that Generative AI has a huge application not just for text, but also for images, videos, audio generation, and much more. AI chatbots like ChatGPT, Google Bard, Bing Chat, etc. leverage Generative AI. It can also be used for autocomplete, text summarization, virtual assistant, translation, etc. To generate music, we have seen examples like Google MusicLM and recently Meta released MusicGen for music generation.


Apart from that, from DALL-E 2 to Stable Diffusion, all use Generative AI to create realistic images from text descriptions. In video generation too, Runway’s Gen-1, StyleGAN 2, and BigGAN models rely on Generative Adversarial Networks to generate lifelike videos. Further, Generative AI has applications in 3D model generations and some of the popular models are DeepFashion and ShapeNet.

Limitations of Generative AI

While Generative AI has immense capabilities, it’s not without any failings. First off, it requires a large corpus of data to train a model. For many small startups, high-quality data might not be readily available. We have already seen companies such as Reddit, Stack Overflow, and Twitter closing access to their data or charging high fees for the access. Recently, The Internet Archive reported that its website had become inaccessible for an hour because some AI startup started hammering its website for training data.

Apart from that, Generative AI models have also been heavily criticized for lack of control and bias. AI models trained on skewed data from the internet can overrepresent a section of the community. We have seen how AI photo generators mostly render images in lighter skin tones. Then, there is a huge issue of deepfake video and image generation using Generative AI models. As earlier stated, Generative AI models do not understand the meaning or impact of their words and usually mimic output based on the data it has been trained on.

It’s highly likely that despite best efforts and alignment, misinformation, deepfake generation, jailbreaking, and sophisticated phishing attempts using its persuasive natural language capability, companies will have a hard time taming Generative AI’s limitations.

Automation Without Intelligence Is Just Not Smart

Automation Without Intelligence is Just Not Smart Ben Bradley

Director of Strategic Accounts – Client Consulting

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The numbers are clear. Most marketers (nearly 70% of businesses) are using some type of Automation system. While there are many success metrics supporting the use of these systems (here and here are just a couple examples) this post isn’t about why you need to use an automation tool – it’s about how you can make it better.

The thing that that is missing from most intelligence systems is insight into what happens beyond your brands touch points. Most intelligence systems can tell you what is happening on your site, or with the brand engagements that you load.

However as marketers we need to remember the average short list of a B2B IT buyer consists of 2-3 vendors. We need to remove the mindset that the only relevant research our accounts/contacts have is with our brand.

The core theme that is repeated every single time is that an IT buying account will always engage with editorial content and other vendors equally, if not more than, with your brand. It happens to all of us, it’s a natural part of the research process to gather un-biased info, and then compare solutions and vendors.  The proof, when we looked at over 4,000 confirmed IT Buying projects and their content consumption journeys we saw:

70% of their content consumption journey was with Editorial content (Tips, news, eGuides, eZines, etc.) on the TechTarget network

30% of their content consumption journey was with Vendor content on the TechTarget network

Your view into the buyer’s content journey is further reduced when you consider that the average IT buyer looked at content from about 17 vendors, and has a short list of about 2 or 3 vendors. This reduces your part of the 30% vendor content consumption quickly. Intelligence is only truly gained when you get a view into ALL this activity.

Segmentation is the Holy Grail

One of my favorite marketers Avinash Kaushik has written a lot about the value of segmentation.  A sophisticated marketer is going to segment nurture streams to offer different content and experiences based on their user’s/accounts engagements. The key to segmentation is being able to pull in as many data points as possible.

Automation without intelligence

Let’s look at the above account journey example to see how you might nurture a contact from this journey. You could segment by:

One of The 11 assets they have engaged with

The event data you have (location, contact)

The site visits they had (when maybe who if they completed a reg page).

Automation with intelligence

Automation without intelligence has already been proven to drive massive returns, now imagine how much more effective it can be if you have many additional data points to segment by:

ALL Contacts at the account that have engaged in relevant research, not just those that download Your content

Priority ranking of all accounts based on key signals and research volume

Insight into which competing vendors they are downloading content from and the volume of engagement they have with each

Size and locations of the buying team, and changes to the team over the last 30,60,90 days

Key pain points and the frequency of research on those (IE – data storage management vs. disaster recovery)

The nurture stream and lead management process could dramatically change with this example:

You could launch a competitive attack campaign based on the knowing that Named Account X has downloaded 25% of their content from Competitor Y

You could deliver thought leadership content in the perfect cadence

Expand your account reach and influence on an account level with access to net new contacts

Adjust nurture streams based the volume and types of content the brand engaged with holistically

Use “signals” such as “Late Stage Signal” to identify when account view product spec sheets, demos and other late buy cycle stage content.

Deliver content based on specific pain points

All of this is based on the account journey across your content, your competitor’s content, and editorial content so you are given a better view into the buyer’s journey.

Intelligence only works when it’s actionable

Intelligence should be the foundation of your marketing strategy. Today’s marketers need to make sure they have access actionable intelligence. Actionable intelligence is being able to message contacts based on every step every step of their content journey, even those steps that are not with you.

Account-Based Marketing, audience segmentation, B2B marketing, content, marketing intelligence

7 Ways Digital Marketing Is Changing The Landscape Of Marketing

7 Ways Digital Transformation is Changing Marketing

Did you know that 4.9 billion people globally use the Internet every day? It makes up about 62% of the world’s population. Imagine the immense marketing possibilities for e-commerce merchants! The seven most effective ways digital marketing is changing the game of marketing are listed as follows.

1. Target the Right Audience 2. Enhances Market Reach

Digital marketing is entirely an online activity and, therefore, has a greater potential of reaching a large audience base across different nations. As a result, startups and small businesses can get a golden opportunity to boost their market reach by gaining wide exposure, as opposed to traditional marketing, which has its geographical limitations.

Moreover, digital marketing has improved interactivity with prospective buyers and helped marketers build strong customer relationships. It helps improve brand awareness and trust, thus influencing the audience’s buying decisions. This, in turn, helps ecommerce grow and generate higher ROI.

Digital platforms are ensuring better connections with consumers as they can interact with each other in real-time over various platforms, such as social media, website chat, emails, live videos, and more. It enables potential buyers to hear the voice behind a brand, thus increasing brand awareness. In other words, through interactive communication, your customers can start identifying your brand by associating it with a thing they love or relate to. In the long run, it creates a buzz around your product or service, thus increasing brand loyalty.

3. Measurable Results

By gaining accurate insights, you can understand which strategies and ad campaigns are working and which are not. You can also identify potential loopholes in any strategy and, hence, correct them on time to boost outcomes. Besides, from data analytics, you can also learn some vital market information, such as −

where your customers are coming from

which keywords are performing the best

what are your consumers’ buying patterns?

your page views

the conversion rates

number of returning customers

cost per lead

Contrastingly, in traditional marketing, you can only ask a few questions like “How did you find us?” or “What products do you love?”. That’s why digital marketing has an extensive scope of doing business, as it allows marketers to gain a complete understanding of the market demands and competition.

4. Cost-efficient 5. Offers Greater Personalization

Since digital marketing enables you to gather crucial customer data, such as their name, contact details, demographics, buying patterns, likes, preferences, etc., you can create better-targeted ad campaigns to reach the right audience. You can create personalized emails and SMS texts to provide discounts and offers on their birthdays, anniversaries, etc. In other words, digital marketing enables you to create customized ad messages, making customers think that you really care about their needs and concerns.

6. Boost Brand Loyalty

eCommerce brands that are digitally active, with their regular updates on blog pages and social media posts, can gain higher trust and credibility in the market. Keeping your customers updated increases your visibility and reputation among your target audience and improves your site rankings. Google and other search engines prefer those websites that provide high user experiences with impactful, engaging, and informative content, thus ranking you favorably on the search engine results pages (SERPs).

7. Effective Competitor Analysis

Competitor analysis is a crucial part of any marketing strategy, as it enables businesses to understand their current market standing. Besides, it also helps digital marketers identify their strengths and weaknesses compared to their rivals. By performing competitor analysis, eCommerce merchants can keep track of the keywords they are ranking for and also understand the type of content their customers are preferring.

By determining the keywords your competitors are working on, you can choose the keywords that have less competition, thus gaining an upper hand in your target market. Furthermore, you can train your digital marketing team to monitor marketing strategies, metrics, and ad campaigns of your rivals, thus making necessary improvements in your approaches.

Final Words

To conclude, digital marketing has taken the world of marketing to a new level and has huge potential in the upcoming years. Successful marketing is a combination of multiple tactics and actions. Since digital marketing offers higher opportunities, online marketers can achieve greater success compared to traditional marketing techniques. We hope the above approaches will help you make smarter decisions and transform your marketing styles to boost business growth!

How Google Is Powering The World’S Ai

Google unveiled its second-generation TPU at Google I/O earlier this year, offering increased performance and better scaling for larger clusters. The TPU is an application specific integrated circuit. It’s custom silicon designed very specifically for a particular use case, rather than a general processing unit like a CPU. The unit is designed to handle common machine learning and neural networking calculations for training and inference; specifically matrix multiply, dot product, and quantization transforms, which are usually just 8 bits in accuracy.

While these kinds of calculations can be done on a CPU and sometimes even more efficiently on a GPU, these architectures are limited in terms of performance and energy efficiency when scaling across operation types. For example, IEEE 754 8-bit integer multiplication optimized designs can be up to 5.5X more energy and 6X more area efficient than 16-bit floating-point optimized designs. They’re also 18.5X more efficient in terms of energy and 27X smaller in terms of area than 32-bit FP multiply. IEEE 754 being the technical standard for floating point computations used in all modern CPUs.

What being an “AI first” company means for Google


Furthermore, many neural networking use cases require low latency and almost instantaneous processing times from a user perspective. This favors dedicated hardware for certain tasks, as opposed to trying to fit typically higher latency graphics architectures to new use cases. Memory latency accessing external RAM can be hugely costly too.

Earlier in the year, Google released a comprehensive comparison of its TPU’s performance and efficiencies compared with Haswell CPUs and NVIDIA Tesla K80 GPUs, giving us a closer look at the processor’s design.

In terms of numbers, Google’s TPU can process 65,536 multiply-and-adds for 8-bit integers every cycle. Given that the TPU runs at 700MHz, it can compute 65,536 × 700,000,000 = 46 × 1012 multiply-and-add operations or 92 TeraOps (trillions of operations) per second in the matrix unit. Google says that its second generation TPU can deliver up to 180 teraflops of floating point performance. That’s significantly more parallel throughput than your typical scalar RISC processor, which usually only passes a single operation with each instruction over a clock cycle or more.

The 16-bit products of the Matrix Multiply Unit are collected in the 4 MiB of 32-bit Accumulators below the matrix unit. There’s also a unified buffer of 24MB of SRAM, which work as registers. Instructions to control the processor are sent from a CPU to the TPU via the PCIe bus. These are complex CISC type instructions in order to run complex tasks which each instruction, such as numerous multiply-add calculations. These instructions are passed down a 4-stage pipeline. There are only twelve instructions for the TPU in total, the five most important of which are simply to read and write results and weights in memory, and to begin a matrix multiply/convolution of the data and weights.

Working with Intel for edge compute

Google’s hardware efforts have given it a major head start in the cloud space, but not all AI applications are well suited to transferring data such great distances. Some applications, such as self driving cars, require almost instantaneous compute, and so can’t rely on higher latency data transfers over the internet, even if the compute power in the cloud is very fast. Instead, these type of applications need to be done on device, and the same applies for a number of smartphone applications, such as image processing on RAW camera data for a picture.

Google’s Pixel Visual Core is primarily designed for HDR image enhancement, but the company has touted its potential for other future machine learning and neural networking applications.

With the Pixel 2, Google quietly launched its first attempt at bringing neural networking capabilities to dedicated hardware suitable for a lower power mobile form factor – the Pixel Visual Core. Interestingly, Google teamed up with Intel for the chip, suggesting that it wasn’t entirely an in-house design. We don’t know exactly what the partnership entails; it could just be architectural or more to do with manufacturing connections.

Intel has been buying up AI hardware companies, nabbing Nervana Systems in 2023, Movidius (which made chips for DJI drones) last September, and Mobileye in March 2023. We also know that Intel has its own neural networking processor in the works, codenamed Lake Crest, which falls under its Nervana line. This product was the result of Intel’s purchase of the company of the same name. We don’t know a lot about processor, but it’s designed for servers, uses a low-precision number format called Flexpoint, and boasts a blazing fast memory access speed of 8 Terabits per second. It’s going to compete with Google’s TPU, rather than it’s mobile products.

What is machine learning?


Even so, there appear to be some design similarities between Intel and Google hardware based on images floating around online. Specifically, the multi-core configuration, use of PCIe and accompanying controller, a management CPU, and close integration to fast memory.

At a glance, the Pixel’s hardware looks quite different to Google’s cloud design, which isn’t surprising given the different power budgets. Although we don’t know as much about the Visual Core architecture as we do about Google’s Cloud TPUs, we can spot some similar capabilities. Each of the Image Processing Units (IPUs) inside the design offers 512 arithmetic logic units, for a total of 4,096.

Again, this means a highly parallelized design capable of crunching lots of numbers at once, and even this trimmed down design can perform 3 trillion operations per second. Clearly the chip features a far smaller number of math units than Google’s TPU, and there are no doubt other differences as this is primarily designed for imaging enhancements, rather than the variety of neural networks Google is running in the cloud. However, it’s a similar, highly parallel design with a specific set of operations in mind.

Whether Google sticks with this design and continues to work with Intel for future edge compute capabilities, or returns to relying on hardware developed by other companies remains to be seen. However, I would be surprised if we don’t see Google’s experience in neural networking hardware continue to evolve silicon products both in the server and small form factor spaces.

Wrap Up

The future according to Google: AI + hardware + software = ?


Google may be best known for its software, but when it comes to powering this new generation of AI computing, Google is equally embedded in the hardware development and deployment side.

The company’s custom TPU silicon provides the necessary energy efficiency savings needed to deploy machine learning on a large cloud scale. It also offers up notably higher performance for these specific tasks than more generalized CPU and GPU hardware. We’re seeing a similar trend in the mobile space, with SoC manufacturing increasingly turning to dedicated DSP hardware to efficiently run these mathematically intensive algorithms. Google could become a major hardware player in this market too.

We’re still waiting to see what Google has in store for its first generation smartphone AI hardware, the Pixel Visual Core. The chip will soon be switched on for faster HDR processing and will no doubt play a role in some further AI tests and products that the company rolls out to its Pixel 2 smartphones. At the moment, Google is leading the way forward with its Cloud TPU AI hardware and software support with TensorFlow. It’s worth remembering that Intel, Microsoft, Facebook, Amazon, and others are all vying for a piece of this quickly emerging market too.

With machine learning and neural networks powering an increasing number of applications both in the cloud and on edge devices like smartphones, Google’s early hardware efforts have positioned the company to be a leader in this next generation field of computing.

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