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Role Of AI in Transforming Customer Service

AI in Transforming Customer Service

Any business expert can tell you the importance of customer service. Technological transformation in work environments is inevitable for standing out in the competition. 

A study shows that due to satisfactory customer service, 42% of business-to-customer customers increased their interest in after-sales purchasing. On the other hand, 52% of them left the brand due to a bad customer service experience. 

Regardless of your industry, from taking orders to underwriting in the insurance industry, AI can revolutionize customer service and many other aspects. The insurance industry is the most beneficiary of AI-integrated customer service. It includes complex data management and fraud detection using a predictable machine-learning model. Artificial intelligence in insurance claims management can deliver tasks error-free and speedily. It would streamline the customer journey for claims, providing a friendly experience for insurers and policyholders.

How AI is Changing the Mood of Employees

Chatbots

Chatbots are AI-integrated, machine-learning operatives that can replace human customer service agents. Expanding a business requires handling customer queries hand in hand, which can be challenging. 

Customers dislike waiting more than 50 seconds to respond to their queries so that AI can give instant and specialized responses. This not only speeds up the process but also makes it accurate and efficient. You can train your chatbot according to your needs and general queries.

Virtual Assistants

Besides helping customers directly, AI can help customer service agents to help through repetitive tasks. Monotonous tasks like analyzing customer feedback data can be handled with AI. 

It would make positive and satisfactory changes in the work environment, boosting the overall mood of the employees.

Predictability

AI can predict consumer behavior based on their past interaction with your firm. This behavior can be used to upsell related products or services. AI can analyze all this by measuring and analyzing their social sharing, likes and dislikes, and common queries. 

Machine learning can analyze big data that would take several days into seconds. This would reduce the cost of human labor and the possibility of error.

How AI is Revolutionary For Customers

All-time active support

Human labor needs breaks on holidays or festivals. AI can work 24/7 for all the days of the year. You don’t have to update your human customer service area to more prominent people, as AI can handle multiple queries simultaneously without hallucinations.

From solving complex queries to ordering pizza, AI can be trained and customized according to your needs. This would also break the language barrier and improve international customer service.

Understanding Customer’s Needs

People need clarification when making big decisions, especially if they are involved in changing insurance firms or investing in stocks. AI can provide scientific and rational reasons to act in a certain way, making the custom journey smoother and more beneficial. 

It can also provide detailed comparisons of different products, analyze the consumer’s specifications, and analyze specific trends to provide customers with the best seasonal hot deals.

NLP Analysis

Natural language processing (NLP) is a critical component of customer service. Its importance can be measured by the fact that Google uses NLP to analyze the pages, position them to analyze pages, and give them positions on search engines’ result pages. 

AI can effectively measure the customer’s tone and specific keywords in voice intelligence. Sentiment analysis of customer feedback is critical in assessing what your customers think of your services or products.

Benefits of using AI in Customer Service

Cost Effectiveness

Replacing human labor with AI can significantly reduce the cost of management. According to a survey, AI chatbots can save up to $8 billion yearly in corporate expenses. You will need fewer employees, and the cost of electricity, hardware, and buildings will be lessened.

Reliability

Top-notchTop-notch customer service is a critical factor in retaining and attracting new customers. Only businesses with the highest level of customer satisfaction would survive in this competitive market. You must integrate AI into your work environment to stay on top of this competitive customer acquisition race.

Personalization

AI can remember previous chats with users to give the desired information or recommendations based on their history. Using this personalized approach will significantly increase the customer retention ratio.

Challenges in Integrating AI in Customer Service Centres

Require Investment

Transforming the work environment can be costly. Expenses include hardware, training staff, software development costs, and more. The exact price would depend on various factors, including requirements, firm size, and location. 

However, business owners should consider the ROI, which can be huge. In addition to the financial gains, it can provide an efficient, deductive, high-quality customer experience.

Training For The New Environment

If you are new to this innovation, many challenges are ahead. AI needs extensive data to train itself for specialized tasks. Moreover, replicating the existing hardware with an integrated AI will cause tech hurdles and challenges.

Data Privacy

With every technological advancement comes the responsibility for data protection from potential threats. You must comply with all the privacy laws of your state. Rules like the General Data Protection Regulation (GDPR) can result in legal action against you if not considered.

Customer Trust

Every novelty takes time to become fully accepted in society. AI is also not the exception. Customers may be skeptical of innovation and hesitate to use AI instead of human agency. They also need time to become aware of its use. 

Efficient use can only be applicable with its basic understanding. You can give a manual or video guide describing all the potential usage of AI in customer service.

Customer Experience

User experience is crucial to the success of any software or website. It should be mobile-friendly, as more than 60% of users use mobile devices. Practical strategies like using breadcrumbs can give customers real-time guidance.

Conclusion

Satisfactory customer service is crucial for a business to survive. It increases brand loyalty and retention. Beyond merely responding to queries, it can be used creatively, like giving customers personalized gifts. 

Remember that the human touch to any technological transformation can not be ignored. You will eventually need the trained staff to maintain AI-empowered technology.

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Can AI exist in a sustainable way?

AI in a sustainable way

The fictional but prescient Dr. Ian Malcom noted in 1993’s Jurassic Park, “…your scientists were so preoccupied with whether or not they could that they didn’t stop to think if they should.”  Generative AI’s rapid expansion due to the increase in size of large language models has felt something akin to genetically engineering a TRex.  It is certainly fun and exciting, but what are the consequences?  

Hardware is also beefing up those CPUs, GPUs, TPUs (all the PUs really) to support the training and distribution of those models. But just as history, and our favorite science fiction movies, have showed us, there is a cost. Of course, we’re all wary that SkyNet (T)  might emerge, (and frankly time will tell), but the more pressing matter is the consumption of electricity and water.

Addressing the AI elephant in the room

At Cisco, we’ve been baking predictive AI into our platforms for years, helping IT operations make insightful, and even proactive, decisions. Across compute, storage, and networking infrastructure, application of predictive AI and observability has been incredibly useful in helping organizations scale and optimize their actual infrastructure usage. With APIs paving the way for multi platform integration, we’re seeing wholesale Day 0 to Day N solutions that help organizations manage usage and more.

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What the research says

While these gains are exciting, the underlying machine learning technologies that support predictive AI do not have the same resource needs as Generative AI, which requires new approaches to reducing carbon footprint and overall environmental impacts

Over the last five years or so, researchers at universities like Berkeley and the University of Massachusetts saw past the horizon and started experimenting and proving methods that could be employed to lessen the energy consumption (and carbon footprint) of precursor technologies like natural language processing (NLP) to large language model (LLM). They even go as far as to prescribe both software/algorithm and hardware/infrastructure improvements to alleviate the carbon footprint created by training and using NLP and LLM. Even better, similar activities are underway to measure the impact of AI technology on water usage as well.

But, that’s not the whole story..

As of today, the true nature of AI’s impact on energy consumption is REALLY hard to actually quantify. Article after article tries to dig into the actual effect of using generative AI technologies. The challenge is that the combination of large amounts of variables (what task is being done, how is the data center setup, what processors are being used, etc. etc.) and IP secrecy (there is a LOT of money to be made here) makes reaching a true, tangible answer difficult. Not to mention, there is no way of knowing if those running LLM-based services are employing some of the proven mitigations noted above. 

The best any of the current research can come up with is energy usage comparable to an average U.S. home per year to the average mid-size country. That’s an unmanageable range which makes  understanding the actual impact and ways to mitigate difficult to identify.

So, it seems, that at least in the short term, newer AI technologies will have an increased impact on energy consumption and water usage to the possible negative detriment of the environment.

Problem solving, the developer way

 So how can AI exist in conjunction with sustainability efforts? Ah, that’s the interesting part. AI just may be the answer to its own problems. The problem that I mention above about it being difficult to figure out the impact of AI usage on energy and water consumption is being currently worked on by AI sustainability initiatives. 

In theory, the models would then be able to suggest solutions to increased water and electricity consumption. r In a slightly less sophisticated model, predictive AI elements are starting to be used to simply just turn things off. This is the simplest answer: eliminate situations where energy is generated but not actually used– and the really cool thing is AI can help us with that.

In the realm of this technological advancement, developers are bestowed with an extraordinary opportunity to make a real impact for a sustainable future.

Getting involved

Cisco’s Build for Better coding challenge, is open March 14 – April 22, 2024, and invites all Developers to harness their skills in AI, Observability, and Sustainability to make a real-world impact. Learn more and commit your code by Earth Day.

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Analysis

Machine learning developers and their data

Machine learning developers and their data

The data science (DS), machine learning (ML), and artificial intelligence (AI) field is adapting and expanding. From the ubiquity of data science in driving business insights, to AI’s facial recognition and autonomous vehicles, data is fast becoming the currency of this century.  This post will help you to learn more about this data and the profile of the developers who work with it. 

The findings shared in this post are based on our Developer Economics 20th edition survey, which ran from December 2020 to February 2021 and reached 19,000 developers. 

Before you dive into the data, our new global developer survey is live now. We have a set of questions for machine learning and AI developers. Check it out and take part for a chance to have your say about the most important development trends and win prizes.

It takes all types

The different types of ML/AI/DS data and their applications

We ask developers in ML, AI, and DS what types of data they work with. We distinguish between unstructured data — images, video, text, and audio — and structured tabular data. The latter group includes tabular data that they may simulate themselves. 

With 68% of ML/AI/DS developers using unstructured text data, it is the most common type of data these developers work with; however, developers frequently work with multiple types of data. Audio is the most frequently combined data type: 75-76% of those that use audio data also use images, video, or text. 

“Unstructured text is the most popular data type, even more popular than tabular data”

Given the most popular applications of audio data are text-to-speech generation (47%) and speech recognition (46%), the overlaps with video and text data are clear. Image data, like audio, overlaps heavily with video data: 78% of those using video data also use image data. The reverse dependence isn’t as strong: only 52% of those using image data are also video data users. The top two applications of both these data types are the same: image classification and facial recognition. These are two key application fields driving the next generation of intelligent devices: improving augmented reality in games and underpinning self-driving cars, in home robotics, home security surveillance, and medical imaging technology. 

The types of data ML/AI/DS developers work with

69% of  ML/AI/DS developers using tabular data also use unstructured text data

With 59% usage, tabular data is the second most popular type of data. 92% of the tabular data ML/DS/AI developers use is observed, while the other 8% is simulated. The two most common use-cases for this data is workforce planning — 39% of developers who use simulation do this —  and resource allocation, also at 39%.

Structured tabular data is least likely to be combined with other types of data. Although uncommon to combine this type of data with audio or video data, 69% do combine tabular data with unstructured text data. The top application of both tabular data and unstructured text is the analysis and prediction of customer behaviour. This is the sort of analysis often done on the data nuggets we leave behind when searching on retail websites — these are key inputs to algorithms for natural language and recommender systems.

Keeping it strictly professional?

The professional status of ML/AI/DS developers

The professional / hobbyist / student mix in the ML/AI/DS ecosystem

ML/AI/DS developers engage in their fields at different levels. Some are professionals, others students or hobbyists, and some are a combination of the above. The majority (53%) of all ML/DS/AI developers are professionals — although they might not be so exclusively. 

Of all the data types, audio data has the highest proportion of professional ML/DS/AI developers. 64% of ML/AI/DS developers who use this type of data classified themselves as a professional; and the majority (50%) of these professionals are applying audio data to text-to-speech generation. The high proportion of professionals in this field might be a byproduct of co-influencing variables: audio data is the data type most frequently combined with other types, and professionals are more likely to engage with many different types of data. 

Data types popular with students include image, tabular, and text data. Between 18-19% of developers who work with these types of data are students. There are many well-known datasets of these types of data freely available. With this data in hand, students also favour certain research areas. 

Image classification, for example, is popular with developers who are exclusively students: 72% of those students who use image data use it for this application, in contrast to just 68% of exclusive professionals that do. In applying unstructured text data, 38% of exclusive students are working in Natural Language Processing (NLP), while 32% of exclusive professionals are. As these students mature to professionals, they will enter industry with these specialised skills and we expect to see an increase in the practical applications of these fields, e.g. chatbots for NLP. 

“65% of students and 54% of professionals rely on one or two types of data”

Besides differences in application areas, students, hobbyists, and professionals engage with varying types of data. 65% of those who are exclusively students use one or two types of data, while 61% of exclusively hobbyists and only 54% of exclusively professionals use one or two types. Developers who are exclusively professionals are the most likely to be relying on many different types of data: 23% use four or five types of data. In contrast, 19% of exclusively hobbyists and 15% of exclusively students use four to five types. Level of experience, application, and availability of datasets all play a role in which types of data an ML/AI/DS developer uses. The size of these datasets is the topic of the next section.

Is all data ‘big’?

The size of ML/AI/DS developers’ structured and unstructured training data

The hype around big data has left many with the impression that all developers in ML/AI/DS work with extremely large datasets. We asked ML/AI/DS developers how large their structured and unstructured training datasets are. The size of structured tabular data is measured in rows, while the size of unstructured data — video, audio, text — is measured in disc size. Our research shows that very large datasets aren’t perhaps as ubiquitous as one might expect. 

“14% of ML/AI/DS developers use structured training datasets with less than 1,000 rows of data, while the same proportion of developers use data with more than 500,000 rows” 

The most common size band is 1K – 20K rows of data, with 25% of ML/AI/DS developers using structured training datasets of this size. This differs by application type. For example, 22% of those working in simulation typically work with 20K – 50K rows of data; while 21% of those working with optimisation tools work with 50K – 100K rows of data. 

Dataset size also varies by professional status. Only 11% of exclusively professional developers use structured training datasets with up to 20K rows, while 43% of exclusively hobbyists and 54% of exclusively students use these small datasets. This may have to do with access to these datasets — many companies generate large quantities of data as a byproduct or direct result of their business processes, while students and hobbyists have access to smaller, open-source datasets or those collected via their learning institutions. 

A further consideration is, who has access to the infrastructure capable of processing large datasets? For example, those who are exclusively students might not be able to afford the hardware to process large volumes of data. 

Non-tabular data is a useful measure for comparisons within categories: for example, 18% of image datasets are between 50MB-500MB, while only 8% are more than 1TB in size. The measure doesn’t, however, allow for cross-type comparisons, since different types of data take up different amounts of space. For example, 50MB of video data takes up a considerably shorter length of time than 50MB of audio data. 

The size of unstructured training data ML/AI/DS developers work with

The categorisation of the different data sizes was designed to take into account the steps in required processing power. For most ML/AI/DS developers, we expect that a 1-25GB dataset could be handled with powerful, but not specialised, hardware. Depending on the language and modelling method used, 25GB on disc relates to the approximate upper bound in memory size that this type of hardware could support. 

We see that 26% of ML/AI/DS developers using text data and 41% using video data will require specialised hardware to manage their training. The high level of specialized hardware manifests as a barrier-to-entry: data analysis on these large datasets is beyond an achievable scope without the backing of deep pockets supplying cloud-based technology support or infrastructure purchases.

Want to know more? This blog post explores where ML developers run their app or project’s code, and how it differs based on how they are involved in machine learning/AI, what they’re using it for, as well as which algorithms and frameworks they’re using.

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Where do ML developers run their code?

In this blog post we’ll explore where ML developers run their app or project’s code, and how it differs based on how they are involved in machine learning/AI, what they’re using it for, as well as which algorithms and frameworks they’re using.

Machine learning (ML) powers an increasing number of applications and services which we use daily. For some organisations and data scientists, it is not just about generating business insights or training predictive models anymore. Indeed, the emphasis has shifted from pure model development to real-world production scenarios that are concerned with issues such as inference performance, scaling, load balancing, training time, reproducibility, and visibility. Those require computation power, which in the past has been a huge hindrance for machine learning developers.

A shift from running code on laptop & desktop computers to cloud computing solutions

The share of ML developers who write their app or project’s code locally on laptop or desktop computers, has dropped from 61% to 56% between the mid and end of 2019. Although the five percentage points drop is significant, the majority of developers continue to run their code locally. Unsurprisingly, amateurs are more likely to do so than professional ML developers (65% vs 51%).

By contrast, in the same period, we observe a slight increase in the share of developers who deploy their code on public clouds or mainframe computers. In this survey wave, we introduced multi cloud as a new possible answer to the question: “Where does your app/project’s code run?” in order to identify developers who are using multiple public clouds for a single project.

As it turns out, 19% of ML developers use multi cloud solutions (see this multi-cloud cheat sheet here) to deploy their code. It is likely that, by introducing this new option, we underestimate the real increase in public cloud usage for running code; some respondents may have selected multi cloud in place of public cloud. That said, it has become increasingly easy and inexpensive to spin up a number of instances and run ML models on rented cloud infrastructures. In fact, most of the leading cloud hosting solutions provide free Jupyter notebook environments that require no setup and run entirely in the cloud. Google Colab, for example, comes reinstalled with most of the machine learning libraries and acts as a perfect place where you can plug and play to build machine learning solutions where dependency and compute is not an issue.

While amateurs are less likely to leverage cloud computing infrastructures than professional developers, they are as likely as professionals to run their code on hardware other than CPU. As we’ll see in more depth later, over a third of machine learning enthusiasts who train deep learning models on large datasets use hardware architectures such as GPU and TPU to run their resource intensive code.

Developers working with big data & deep learning frameworks are more likely to deploy their code on hybrid and multi clouds

Developers who do ML/AI research are more likely to run code locally on their computers (60%) than other ML developers (54%); mostly because they tend to work with smaller datasets. On the other hand, developers in charge of deploying models built by members of their team or developers who build machine learning frameworks are more likely to run code on cloud hosting solutions.

Teachers of ML/AI or data science topics are also more likely than average to use cloud solutions, more specifically hybrid or multi clouds. It should be noted that a high share of developers teaching ML/AI are also involved in a different way in data science and ML/AI. For example, 41% consume 3rd party APIs and 37% train & deploy ML algorithms in their apps or projects. They are not necessarily using hybrid and multi cloud architectures as part of their teaching activity.

The type of ML frameworks or libraries which ML developers use is another indicator of running code on cloud computing architectures. Developers who are currently using big data frameworks such as Hadoop, and particularly Apache Spark, are more likely to use public and hybrid clouds. Spark developers also make heavier use of private clouds to deploy their code (40% vs 31% of other ML developers) and on-premise servers (36% vs 30%).

Deep learning developers are more likely to run their code on cloud instances or on-premise servers than developers using other machine learning frameworks/libraries such as the popular Scikit-learn python library. 

There is, however, a clear distinction between developers using Keras and TensorFlow – the popular and most accessible deep learning libraries for python – compared to those using Torch, DeepLearning4j or Caffe. The former are less likely to run their code on anything other than their laptop or desktop computers, while the latter are significantly more likely to make use of hybrid and multi clouds, on-premise servers and mainframes. These differences stem mostly from developers’ experience in machine learning development; for example, only 19% of TensorFlow users have over 3 years of experience as compared to 25% and 35% of Torch and DeepLearning4j developers respectively. Torch is definitely best suited to ML developers who care about efficiency, thanks to an easy and fast scripting language, LuaJIT, and an underlying C/CUDA implementation.

Hardware architectures are used more heavily by ML developers working with speech recognition, network security, robot locomotion and bioengineering. Those developers are also more likely to use advanced algorithms such as Generative Adversarial Networks and work on large datasets, hence the need for additional computer power. Similarly, developers who are currently using C++ machine learning libraries make heavier use of hardware architectures other than CPU (38% vs 31% of other developers) and mainframes,  presumably because they too care about performance.

Finally, there is a clear correlation between where ML developers’ code runs and which stage(s) of the machine learning/data science workflow they are involved in. ML developers involved in data ingestion are more likely to run their code on private clouds and on-premise servers, while those involved in model deployment make heavier use of public clouds to deploy their machine learning solutions. 31% of developers involved across all stages of the machine learning workflow – end to end – run code on self hosted solutions, as compared to 26% of developers who are not. They are also more likely to run their code on public and hybrid clouds. 

By contrast, developers involved in data visualisation or data exploration tend to run their code in local environments (62% and 60% respectively), even more so than ML developers involved in other stages of the data science workflow (54%).

Developer Economics 18th edition reached 17,000+ respondents from 159 countries around the world. As such, the Developer Economics series continues to be the most global independent research on mobile, desktop, industrial IoT, consumer electronics, 3rd party ecosystems, cloud, web, game, AR/VR and machine learning developers and data scientists combined ever conducted. You can read the full free report here.

If you are a Machine Learning programmer or Data Scientist, join our community and voice your opinion in our current survey to shape the next State of the Developer nation report.

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The Significance of AlphaGo: Has a golden age for artificial intelligence just dawned?

artificial intelligence

In recent years artificial intelligence (AI) has returned to the forefront of technological debate. That debate has moved on from when, and even whether, computers will ever display intelligent behaviour to how smart they will get, how quickly, and what the implications are for society. Although there are multiple approaches to creating AIs, the ones that involve machine learning from large datasets are generally outperforming all others. The results from such systems are often so impressive that large companies are rushing to hire data scientists, collect more data, and apply the latest machine learning techniques to inform their management decision making. Google’s DeepMind team recently demonstrated that without any human in the loop they can build a system that makes complex strategic decisions better than a human expert. Their approach suggests a way forward for building such systems in many diverse fields.

Machine_learning&artificial_intelligence

The game computers couldn’t beat

The announcement from the DeepMind team that their AlphaGo program had defeated the European champion at the game of Go was a highly significant landmark in AI. Not only did they accomplish a long-standing ‘grand challenge’ in AI and surpass rival Facebook’s efforts by an enormous distance, but the way the system works is in many ways very human-like. At first glance it’s easy to dismiss game-playing AI systems as not immediately applicable to real-world problems. The ‘world’ the AI operates in is incredibly simple compared to our physical world – in the case of Go, a 19×19 board where a black or white stone can be placed on each intersection. However, [tweetable]advances in AI from the pursuit of better Go playing programs are already being used[/tweetable] in real-world applications elsewhere. Also, the ‘deep convolutional neural networks’ that AlphaGo uses to ‘perceive’ the board are similar to those currently being employed to push forward the state-of-the-art in image and speech recognition, as well as natural language processing.

It’s different this time

Back in 1997, IBM’s Deep Blue beat the world Chess champion, Garry Kasparov. How is this different? First, Go is significantly more complex than Chess. There are nearly an order of magnitude more moves possible from every position and each move can have a bigger impact on the strength of a player’s position. Second, Deep Blue used a supercomputer and some hand-crafted heuristics to effectively do a brute force search of all reasonable future move combinations to pick the best move to make next. This was nothing like the way a human would play Chess and also not generalisable to other problems.

In contrast to Deep Blue, AlphaGo combines two deep convolutional neural networks with a Monte Carlo Tree Search algorithm to select moves in a way that’s quite similar to the way a human would play. The first neural network, called the policy network, picks a few promising positions for the next move. A human player doesn’t systematically evaluate all possible moves, rather through experience they develop an intuition for moves that should make their position stronger. They would struggle to explain why they selected a specific move over others in many cases. This suggests they’ve developed a model for how to play that exists below their conscious awareness. AlphaGo’s policy network is trained to predict the moves that expert players would make using a dataset of 30 million different positions from real games. The second neural network, called the value network, estimates how strong any given position is. It was trained, simplifying slightly, using the results of the policy network alone playing against itself from 30 million distinct positions. The Monte Carlo Tree Search is then used to look ahead from each move selected by the policy network at the opponent’s likely responses and AlphaGo’s subsequent moves. However, rather than search all the way to the end of the game, the value network is used to evaluate the end position after a sequence of moves. This is also similar to human play, looking a handful of moves ahead to assess the probability of gaining an advantage with each possible move. The lookahead searches are shallow (constrained by the processing power and time allowed for a move) and yet the results are better than existing leading systems that look much further ahead but with much less sophisticated move candidate selection and position evaluation capabilities.

Widely applicable artificial intelligence

This might all still sound a long way from a truly human style of thinking but if we abstract and generalise it slightly then it becomes more familiar. For any goal-oriented behaviour in a complex or changing environment we can assess our current situation versus our goal and generate some options for moving towards the goal. We can then simulate or predict the results of taking those actions and evaluate the new situations we could get into. We choose the option that moves us closest to our goal, or has the highest probability of moving us closer to that goal. This is just a description of iterative planning.

AlphaGo has shown that we can train a machine-learning system to emulate the options a human would select in a relatively-complex environment. If we simulate the immediate results of those selections we then just need to evaluate where we get to with each option. Again: machine learning comes to the rescue. If we can acquire or generate enough data we can train another machine learning system to perform the evaluation. None of this is really a new idea but now it has been demonstrated to be good enough to beat a professional at Go, it’s a fair bet it can be made to work for a huge range of other problems too. This is possibly why it’s such a landmark for AI research. It’s a challenge that until very recently was thought to require a completely new breakthrough in AI and probably another decade of research (and Moore’s Law) to get us there. It turns out the techniques we’ve already invented, when suitably combined, can achieve very intelligent behaviour.

Dawn of a golden age?

There’s an outside chance Go just happened to be a lot easier than we thought, or just unusually suited to these ‘deep learning’ techniques. However, given the progress that’s being made with deep learning on other longstanding AI problems it seems more probable that [tweetable]we’re about to enter a golden age for AI[/tweetable]. In this context it’s interesting to note that AlphaGo beat the European champion a month before Google opened the source code for their TensorFlow deep learning framework (which prompted Microsoft to follow suit with theirs). These open source moves can be seen as part of a land grab for talent and mindshare. The techniques are the subject of published research and efficient implementations are valuable but nowhere near as much as the data required for training and the talent to utilise it. Then of course, Google, Microsoft, Amazon and a bunch of startups will all offer managed solutions for training and running these kinds of framework on their clouds. As the significance of DeepMind’s accomplishment sinks in, more researchers and developers will rush to jump on the machine learning bandwagon and there will be no shortage of tech giants waiting to welcome them aboard.