If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
John Carmack is without a doubt one of the best software engineers the world has ever seen. How he fares will ultimately come down to whether our current block on developing AGI is caused by engineering, hardware, or theory (or a combination thereof). If it's just a matter of fitting together the pieces we've already developed in the right way then he honestly has a chance at making some headway. If it turns out we need substantially more computing power or more theoretical insight on the nature of intelligence then this is going to be pretty futile.
A Supreme Court justice has added his voice to calls for the regulation of computer algorithms handling crucial decisions about people's lives. An'expert commission' could help ensure that automated decision making processes have'a capacity for mercy', Lord Sales (Philip Sales QC), said last night. Presenting the British and Irish Legal Information Institute's Sir Henry Brooke Lecture, Lord Sales said the growing role of algorithms and artificial intelligence poses significant legal problems, in particular around the fundamental concept of agency. Existing prejudices could be embedded in hidden rules that are impossible to challenge, he said. 'AI may get to the stage where it will understand the rules of equity and how to recognise hard cases, but we are not there yet.'
Storage management largely revolves around pattern recognition --and AI can help. There's a reason an entire industry is devoted to storage management: No matter what changes have been wrought by hardware and software providers and designers, it's just not an easy task. Performance, provisioning, access control--whatever the task may be--managing business storage is a chore. Perhaps it's the continually changing nature of storage, in performance, capacity, and technologies, or simply the way storage gets used. Effectively managing large amounts of storage has become a specialist role involving not just the right software but detailed knowledge of the storage environment and the way it is being used by those responsible for maintaining and managing the storage and data.
The Chancellor of the High Court has urged commercial lawyers to prepare for the disruptive impact of technology on the law, the legal system and legal profession before others "steal a march" on them. Sir Geoffrey Vos said the profession needed "to turn its incredible intellectual fire-power towards the development of the English common law, so that it can effectively tackle the problems thrown up by the use of big data, cryptoassets, on-chain smart contracts, and artificial intelligence". Expressing confidence that the English common law could adapt to these challenges, he added: "My plea is that you do not leave it too late, because there are many other brilliant lawyers in other jurisdictions who are motivated to steal a march on their common law colleagues in the UK." Giving the Commercial Bar Association's annual lecture this week, Sir Geoffrey warned commercial lawyers that it was too late to hope to retire before any of this became a reality. "It is already reality," he said. Rather, he encouraged lawyers "to think imaginatively about the world in which the commercial legal services of the future will be required".
It has been found that taking a look at standard ECG tests, AI can help identify patients who are more likely to die of any medical reason within a year. The researchers from Geisinger Health System in Pennsylvania reached this conclusion after analyzing the results of 1.77 million ECGs and other records from almost 4,00,000 patients. The team of researchers used that data to compare ML-based models. The model can either directly analyze the raw ECG signals or depend on aggregated human-derived measures and commonly diagnose disease patterns. The neural network model was found to be more efficient which can directly analyze the ECG signals for predicting the one-year risk of death.
Freshworks, a company that provides customer service software and automation tools for businesses, has confirmed that it's raising $150 million in a series H round of funding co-led by Alphabet's CapitalG, Sequioa Capital, and Accel. This takes the company's total funding to nearly $400 million and follows its $100 million series G round last July that included the same three investors. This time around, however, Freshworks is valued at $3.5 billion, more than double its previous valuation. It's worth noting that Freshworks hasn't officially closed the fresh round of funding yet, having merely signed "definitive agreements" with the aforementioned investors -- it doesn't expect the financing to close until the end of the year. It appears the reason Freshworks preannounced the funding was due to a leak last week on an Indian news site via anonymous sources.
What is good for the country might not work for a city and what is good for a city might not work for a small community. Successful cities or communities are usually built via a bottom-up approach, called localism. The archaeological evidence at Harappa, Mohenjodaro and other such sites are a testimony to this. Be it the sanitation or the street connectivity, the planning looks way ahead of its time. However, today, the people of the modern cities struggle to commute, breathe and carry out their routine.
When you see "machine learning" on a vendor's feature list, it's likely that the vendor is using a type of machine learning known as supervised machine learning. This type of machine learning relies on large labeled datasets to train a model. So, as the name suggests, some "supervision" is required. Supervised machine learning has become a popular approach because it is very effective at quickly identifying known cybersecurity threats, such as malware, since we have decades' worth of data on malware and signatures (i.e.
Abstract: We describe a neural network-based system for text-to-speech (TTS) synthesis that is able to generate speech audio in the voice of many different speakers, including those unseen during training. Our system consists of three independently trained components: (1) a speaker encoder network, trained on a speaker verification task using an independent dataset of noisy speech from thousands of speakers without transcripts, to generate a fixed-dimensional embedding vector from seconds of reference speech from a target speaker; (2) a sequence-to-sequence synthesis network based on Tacotron 2, which generates a mel spectrogram from text, conditioned on the speaker embedding; (3) an auto-regressive WaveNet-based vocoder that converts the mel spectrogram into a sequence of time domain waveform samples. We demonstrate that the proposed model is able to transfer the knowledge of speaker variability learned by the discriminatively-trained speaker encoder to the new task, and is able to synthesize natural speech from speakers that were not seen during training. We quantify the importance of training the speaker encoder on a large and diverse speaker set in order to obtain the best generalization performance. Finally, we show that randomly sampled speaker embeddings can be used to synthesize speech in the voice of novel speakers dissimilar from those used in training, indicating that the model has learned a high quality speaker representation.