At the strategy conference of Baidu cloud computing on Wednesday, Baidu launched three intelligent cloud platforms. They will integrate with pre-existing cloud services for its open cloud platforms to help enterprises increase working efficiency. Baidu founder and CEO Robin Li said that Baidu has been a de facto search engine company from the very beginning, but that the company was bound to move into cloud technology, as efficient web searching is made possible via the cloud. Li said that Baidu used to consider cloud computing as too simple a technology and would rather focus on building its web search engine, but then some recent changes happened: On the one hand, the days are gone when economic development is accelerated by a cheap labor force, and companies today must survive using technological innovations and higher efficiency. On the other hand, cloud technology has been making breakthroughs, and it is no longer merely about storage and computing.

AI implementations today are made possible by increased processing power, low cost storage, the ramp-up of cloud computing, mobility, and advanced algorithms to program cognitive theories. Technology companies have already started offering AI platform as a solution or as a service and the cost and expertise needed to make use of these AI platforms is coming down. IT companies aspiring for a piece of the AI market can collaborate in areas of development with cloud service providers, mobile application developers, IT infrastructure service providers and analytics engine providers. AI applications and platforms need microprocessors to execute complex tasks at speed, cloud computing for low cost processing, data storage of massive volumes of unstructured data and smarter analytics engines with Natural Language Processing (NLP), voice and pattern recognition, machine learning, and mobility for wide spread use from remote locations.

Artificial Intelligence has been heralded as a game changer in the drive toward the intelligent enterprise. While AI and machine learning has been around for more than five decades, it's today's increasingly interconnected world and the continuing explosion of data that is driving an increase of applications powered by AI. AI promises to deliver exciting opportunities to the IT as well as the business world, and many believe the reality is not far off. According to a recent report by Research and Markets, the AI market is estimated to grow from 419.7 million in 2014 to 5.05 billion by 2020, at a CAGR of 53.65% from 2015 to 2020. Major factors driving growth include diversified application areas of AI, improved productivity, and increased levels of customer satisfaction.

In a recently accepted manuscript titled "Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data", scientists from Insilico Medicine, Inc located at the Emerging Technology Centers at Johns Hopkins University in collaboration with Datalytic Solutions and Mind Research Network presented a novel approach applying deep neural networks (DNNs) to predict pharmacologic properties of many drugs. In this study, scientists trained deep neural networks to predict the therapeutic use of a large number of drugs using gene expression data obtained from high-throughput experiments on human cell lines. Authors used a sophisticated approach of measuring the differential signaling pathway activation score for a large number of pathways to reduce the dimensionality of the data while retaining biological relevance and used these scores to train the deep neural networks. "The world of artificial intelligence is rapidly evolving and affecting every aspect of our daily life. And soon this progress will be felt in the pharmaceutical industry. We set up the Pharma.AI division to help pharmaceutical companies significantly accelerate their R&D and increase the number of approved drugs, but in the process we came up with over 800 strong hypotheses in oncology, cardiovascular, metabolic and CNS space and started basic validation. We are cautious about making strong statements, but if this approach works, it will uberize the pharmaceutical industry and generate unprecedented number of QALY", said Alex Zhavoronkov, PhD, CEO of Insilico Medicine, Inc.

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DEEPMIND, Google's London-based artificial intelligence company, has started training neural networks to recognise the signs of eye disease in medical images. A partnership with Moorfields Eye Hospital in London has given the company access to about a million anonymised retinal scans, which DeepMind will feed into its artificial intelligence software. The project will target two of the most common eye diseases – age-related macular degeneration and diabetic retinopathy. More than 100 million people around the world have these conditions. Moorfields is providing scans of the back of people's eyes, as well as more detailed scans known as optical coherence tomography (OCT). The idea is that the images will let DeepMind's neural networks learn to recognise subtle signs of degenerating eye conditions that even trained clinicians have trouble spotting.

"Flint seeks to invest in ground-breaking technologies that directly improve human life," said Flint Capital Partner Artem Burachenok. "Findo's ability to mitigate the growing challenge of massive data in everyone's inbox, cloud and storage solution is a game changer." Findo's smart search engine helps users quickly find information buried in a wide array of documents, slides, audio files, images, or any other information from sources as varied as Dropbox, Google Drive, Evernote and Gmail, Exchange and Outlook. Findo search bots can search from Slack, Telegram, Facebook Messenger and Skype and deliver results right to the messenger. "We are excited to have Flint as one of our investors," said Gary A. Fowler, Co-Founder and CEO of Findo.

Notably, one of the big differences between machine learning and computer-assisted analysis (where humans are involved) is that the recent breakthroughs in machine learning enable computers to teach themselves how to solve problems. Now, machine learning enables computers to find answers in ways that are unguided by human intervention. Other examples include Google's self-driving car, how Netflix suggests which movies you should try next, and how a dating site suggests which people are most likely to be a suitable match for you… Unexpected insights As with most technological tools today, almost any company or sector can leverage machine learning to better serve their customers. Companies that are open to innovative ways of finding insights in their data can ultimately serve their customers more efficiently and even develop closer relationships with them in the long-term.

It is no surprise that data analytics and machine learning are fast becoming key components of every innovative company's toolkit, given the massive increase in the amount of data that companies are generating. Because of the sheer volume and complexity of data being created, it is often beyond human capacity to find relevant trends or insights within what has been tagged as'Big Data'. Notably, one of the big differences between machine learning and computer-assisted analysis (where humans are involved) is that the recent breakthroughs in machine learning enable computers to teach themselves how to solve problems. So previously, when humans were directing computers, they were limited to very direct questions and answers (for example, "what is my top selling item?") and required the person using the machine to dictate which method to use to the solve the problem. Now, machine learning enables computers to find answers in ways that are unguided by human intervention.

It's no secret, artificial intelligence (AI) is capable of doing unimaginable things, however understanding how human behave is not one of those, but a team at MIT's Computer Science and Artificial Intelligence Laboratory is keen on changing that. Basically, researchers turned computers into sofa spuds by dishing out 600 hours of footage from some of the most popular TV shows including "Desperate Housewives," "Scrubs" and "The Office," NPR reported. Every clip was made to conclude with either one of the four actions: a hug, a kiss, a high five or a handshake. The computer was then challenged to predict which of the four aforementioned actions was about to happen. With the aid of learning algorithm, the artificially intelligent test subjects successfully predicted the appropriate action 43 percent of the time, which is well below the 71 percent success rate from human test subjects, CNet reported.