Author: Alice

Chatbots and the Rise of Artificial Intelligence

Gravity4 Mona Lisa - Chatbots
Gravity4 Mona Lisa AI  – Powered Chatbots

Post authored by:      MD, Gravity4 Asia

Chatbots have risen in the communication ecosystem so that some say they’ll replace mobile apps and even sites. The history of chatbots starts with ELIZA, an application built at MIT in 1966, that could carry on a text conversation with human interlocutors.

Messaging apps and social media long ago replaced conventional phone calls to connect with people. As the dynamics of communication change, consumers are more apt to use a similar service for customer support issues. A chatbot, in these instances, helps bridge the communication gap between customer and the brand. These bots essentially provide instant feedback and answers to a consumer’s urgent questions. Imagine having a medical question at 3 a.m. and no medical person to call.

AI powered Chatbots
Mona Lisa – Artificial Intelligence Powered Chatbots

This is one area of many areas where chatbots come handy in critical moment to provide consumers with much-needed answers and relief. For commercial brands, these lightweight applications aim to provide value to consumers and assist them in retrieving information as well as get product recommendations and getting information to consumers in a speedy and accurate manner in a world where consumers increasingly demand answers right here right now at any time of the day. Furthermore, these bots serve as trusted and reliable employees, ensuring brand loyalty

Next Immediate Disruption

Chatbots have had a great impact on consumers, so that they experience digital ecommerce and other services in a completely new way through AI-driven conversations. It’s undeniable these bots are the newest trend among the messenger apps. However, these bots’ rise is a mere signal to a huge upcoming disruption in the marketplace.

It’s expected these bots could eventually replace a lot of “call centers” and even sales people within the foreseeable future. Furthermore, they may potentially replace low-level business functions the web or mobile apps currently handle.

Imagine bots powered with machine learning and can recognize speech and data, learn natural language patterns and interpret historical engagement based on previous interactions. Sounds a lot like super-intelligent artificial intelligence, doesn’t it?

As structured data is compiled and imported, these applications hold the capacity to understand the users’ specific needs and provide appropriate solutions without human customer service. The chatbots’ intelligence enables them to handle multiple conversations with ease.

Chatbots: Today

Brands and businesses are racing to adopt (or build) a chatbot for two main reasons. One, the chatbots let businesses talk to individual consumers in real time and engage in automated two-way conversations at scale. The second is data. Yup, a life without Big Data won’t be much of a life on the digital landscape. Data allows businesses to keep a measurable KPI to enhance their customer experiences and optimize the engagement or sales with the hope of future sales.

Our development efforts on these bots with powerful machine learning methods and continuing to improve them to actually understand conversation using natural language processing. With the layered capability of AI-powered engine, we’re focused on enabling these bots to communicate using both text and voice and retrieval of recommendations through searching on the open web.

For companies doing online and even offline sales, we think of this as the next generation search engine that serves your every need knowing your historical purchase patterns, current needs and expectations. Imagine telling chatbot you need coffee and it delivers it to you within the next 30 mins to your doorstep with the exact amount of milk and sugar in it. That is very close to happening in many parts of the world and Asia in particular China is racing to develop AI driven technology and there’s no doubt that Chatbots and voice based applications will play an important role to brining this to the man on the streets and serving their daily needs, ones as simple as getting coffee.

Chatbots: Future

By 2020, it is estimated by Garner that 85% of the consumers will their relationships with brands without ever interacting with a human; every aspect of the interaction – from start to finish – will be AI driven.

Surprisingly, many key regulatory and business decision makers have not considered the implications of transitioning to utility to AI, or even tested it through chatbots for their own businesses and industries. Furthermore, some regulatory bodies have not assessed the deep social implications on the wider marketplace and economy either. Virtual assistants (Siri/Google Assistant), customer service chatbots (Gravity4 Asia/Pixels’ Chatbot), conversational e-commerce assistants (Alexa), autonomous cars, and medical techs – the list of AI powered chatbots is endless.

Whether the brands want to acknowledge it or ignore it, chatbots will continue to revolutionize the way that brands provide service to their customers, increase customer service engagement, brand loyalty and ultimately profits.

At the current pace of the AI powered chatbots, we will continue to see the acceleration of the automation. Furthermore, I feel chatbots potentially be an evolutionary process to a stand-alone mobile app, since they can learn our habits, understands our tastes and preferences and adopt each time we interact with them.

As we continue to evolve AI incrementally, these intelligent bots are drastically minimizing the barrier in making machines intelligently learn from prior interactions and preferences. On this road to discovery we’re singularly intent on ensuring conversational software will enable humans to talk to machines naturally.

Brands and marketers need to sit up and take chatbots seriously. As chatbots become more powerful with AI and machine learning layered on top, they not only will be able to connect you closer to your customers but help transform your business. Stay tuned as AI-powered chatbots continue to become the fastest-growing software segment.

Hong Kong: Adapt or fall behind 

Hong Kong, a city with 238% mobile penetration with now close to 90% smartphone penetration, a population that is addicted to their phones and a market where brands thrive on cutting edge technology, aim to stand out from the clutter and increasingly rising cost in doing business including talent cost, Hong Kong brands have to play a leading role in the development and deployment of chatbots to aide their businesses.

It is vital that automation and deployment of instant customer service take center stage in Hong Kong as its already discerning consumers become even more demanding and expectations increase with every new piece of technology made available.

Amidst a rapidly changing business environment, rapidly rising cost and consumer expectations, Hong Kong brands need to up their game and be there when consumers want them to be. Yes, they can hire more people to deal with mundane questions such as what time does your shop open or how do I fix the display on my television to work around the clock or they can adopt technologies such as chatbot to take care of this. From there on, chatbots with AI can also be your sales rep to up-sell your customer.

All in all, the whole point of creating chatbots is to build superb customer experiences. As they say, the customer is always right and we need to be there for them, always.

 

Alice
I am a Data Engineer and support the APAC region of Gravity4. I have Masters in Computer Science with focus on Data Analytics. I have been working on projects related to machine learning & deep learning.

Path to forge with AI

Artificial Intelligence Wins Again

As we continue to forge our path towards Artificial Intelligence becoming mainstream, it is important to reflect on the breakthrough discoveries along the way. Yesterday marks just that event for Gravity4 Data Science team – when AlphaGo, an AI, outmatched the Chinese Grandmaster Ke Jie in a chess match.

AlphaGo
AlphaGo’s algorithm showcased its instinctively ability to play the game

The event demonstrated the live capacity of a machine to collaborate with the other players for tactics and showcased its mastering of the abstract thought process.  Furthermore, AlphaGo AI demonstrated its instinctively ability to play the game, and taught itself through a self-reinforcement learning process.  

AlphaGo AI played and won Grandmaster Ke Jie in March of 2016 as well. However, yesterday’s match was significant as Ke Jie played the game using some of the tactic AlphaGo had used in one of the prior matches with other players. To Ke Jie’s nice surprise, the program went on to use brilliant new tactics.

“There was a cut that quite shocked me, because it was a move that would never happen in a human-to-human Go match. Last year, it was still quite human-like when it played. But this year, it became like a god of Go.” – Ke Jie

The fundamental idea behind AI is to imitate human brain’s decision-making process. This is where vast majority of our researchers have spent the past 50+ years to understand the mechanism and program it algorithmically inside a machine. Nonetheless, thanks to the cutting-edge digital innovation focused around big data, advances in computational mathematics and increasingly powerful computers, researchers are now constructing additional layers to further complete the simulated “neural network”.

We are super happy at Gravity4 that a machine algorithm won the game playing against the Grandmaster – proving machine learning’s efficiency to self-teach. This is exactly the type of ‘machine training’ our data scientists are incorporating within Mona Lisa!

Follow our blog and learn more on the cutting edge technology that is set to “make people’s lives more productive and creative.

Alice
I am a Data Engineer and support the APAC region of Gravity4. I have Masters in Computer Science with focus on Data Analytics. I have been working on projects related to machine learning & deep learning.

Artificial Intelligence, Big Data, Machine Learning

Gravity4 MonaLisa
#CreateYourMonaLisa

Artificial intelligence, machine learning, deep learning, big data are the buzz words and often are used interchangeably or incorrectly in the advertising ecosystem. Artificial intelligence is used where machine learning should be and machine learning is often confused with data mining. Machine learning is a method of developing algorithms for recognizing patterns within data. Data mining uses techniques developed in machine learning, i.e. machine-learning algorithms, and statistics.

In this blog, I will help provide some clarifications pertaining to advertising platform. Perhaps, view part of the blog as “Artificial Intelligence, Machine Learning and Big Data – 101”. In the future blogs, I hope to introduce you to #MonaLisa, Gravity4’s Intelligent Cloud Marketing Platform.

Understanding the data

With the ever growing libraries of the big data, data mining has been an ongoing task. Platforms need to have the computational power to form meaningful advertising correlation, otherwise, much of this data is of no use. Predominant dimensions of “big data” are characterized in four sectors: 

  • Volume of “big” data (while important) is not the sole principle variable; rather, it is the method in which the platforms organize this data – to create better decisions & strategies for the marketing campaigns.
  • Velocity is the measure of the data inflow capacity.  It is often high, especially for platforms like Gravity4, where the inflow stream is ingesting social, mobile, and sensor based data. Aside from the efficient inflow of this data stream, some variables require real-time processing (ie: geo-location from mobile), which means data stream must be handled in timely manner. 
  • Variety is further of importance to decipher the data from structured and unstructured formats. 
  • Variability of the data, adds interesting complexity as it is routinely inconsistent. A lot of social data depends on the trending news in the media. Hence, makes the variability dimension unpredictable.

In the past few years, machine learning has made a significant breakthrough. Many big organizations now offer low-cost options for storing data, which previously couldn’t be supported by bootstrapped start-ups. We now also have faster processors, high connectivity, high throughputs, virtualization, cloud computing, large grid platforms and clustering, which few years ago were the main restrictions to do advance computations. In additions, we now have distributed big data platforms, like Hadoop, that are open source and affordable.

Importance of AI in marketing

The environment of advertising tech is increasingly complex. We now have more data than to know what to do with it. The role media analysts were able to play in ad-tech, a few years ago, is no longer scalable.  Machine Learning provides depth of insights that data analysts simply can’t. The ongoing challenge is to comprehend the analysis on large volume of data streams, as the potential correlations and relationships between disparate data sources are too large for any human analyst to compute.

For effective audience segmentations, it is no longer analyzing the different websites and influencers, rather the consumers are now constantly switching between devices. This additional layer of complexity adds depth to the data inflow. For this machine learning or ‘machine-assist’ is effective than ‘human-only’ analysis. Machine learning is able to test reasonable hypotheses and derive meaningful insights buried in the libraries much faster than humans. For technology platforms using the advanced computation methods, you have to be able to process this information in real-time and draw statistically relevant ad to engage the consumer.

#MonaLisa
Gravity4 #CreateYourMonaLisa

 

Effects on media buying or RTB

Programmatic media buying is a mechanism through which brands buy audiences. It is a marketplace and RTB is a transaction that occurs on that platform. Think of machine learning as a catalyst to that marketplace. It is now able to match the brands with their current and potential consumers in real-time, across devices, and offer various statically relevant correlations – thereby adding an enhanced degree of certainty for a suggested action.

Machine Learning is NOT a rule-based system that requires an analyst to hard-code domain knowledge into a system. Rather, Machine Learning learns to make decisions based on the inflow of data and experience. As we continue to train the algorithm, we continue to advance the Artificial Intelligence.

So, what does this mean for marketers? Well, brands do not have to worry and figure out the cumbersome process of Machine Learning on their own. They can partner with technology platforms such as Gravity4 and utilize #MonaLisa’s Machine Learning capabilities to stay connected with their consumers. 

Management

The method of analyzing the data varies in each company. Some platforms take in all the data, but don’t know what to do with it. Some will determine subset of variables to look at before the analysis of the data and throw away (or not use) the rest of the data. Either of the processes (and perhaps everything in between) are ways in which companies are forming their confidence in their decision making predictions. At Gravity4, we test various variables. It allows us to have a strategy in place for specific vertical with abundant variables to assess correlation.

Aside from the ad-tech, there are many industries using machine learning. So, the journey to that beautiful world of Artificial Intelligence isn’t a lonely one. You may very well already be interfacing with some technologies. 

  • Retail: exploring AI to best market their customers, up-sell products and enhance customer win-back
  • Healthcare: exploring to find ways to improve delivery of patient care
  • Finance: exploring ways to identify ways to minimize fraud
  • Industrial: aiming to reduce waste and increase efficiency

As we continue to refine the algorithms and advance our understanding of recurrent neural networks and deep learning, we are essentially moving towards Artificial Intelligence.  

Follow our blog to learn more on the series.

Want to learn more about Gravity4’s #MonaLisa, contact your local sales team. We are serving over 20 countries.

Alice
I am a Data Engineer and support the APAC region of Gravity4. I have Masters in Computer Science with focus on Data Analytics. I have been working on projects related to machine learning & deep learning.